THE EFFECTS OF BODY IDEAL PROFILE PICTURES AND FRIENDS’ COMMENTS ON SOCIAL NETWORK SITE USERS’ BODY IMAGE: A SIDE MODEL APPROACH

Mark Allen Flynn

A Dissertation

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

December 2012

Committee:

Sung-Yeon Park, Ph.D, Advisor

Gary M. Heba, Ph. D. Graduate Faculty Representative

Srinivas R. Melkote, Ph.D.

Gi Woong Yun, Ph.D.

© 2010

Mark Allen Flynn

All Rights Reserved

iii ABSTRACT

Sung-Yeon Park, Advisor

Although a substantial body of research has explored the effects of exposure to idealized images in traditional media on one’s body image, there is relatively little systematic investigation of the body ideal on social network sites (SNSs). In an attempt to further body image research, this study sought to explore the effects of exposure to

Facebook body ideal profile pictures and body ideal comments on users’ body image. In addition, the social identity model of deindividuation effects (SIDE) was used to explore

Facebook users’ adherence to a body ideal norm, as well as the role of group identification in this process. The SIDE model has been widely used to investigate group communication in CMC contexts, yet had not been used in SNS research prior to this study.

To address this issue, a pre-test post-test 2 x 2 X 2 between-group web-based experimental design was used on a mock Facebook status page. The design was comprised of body ideal profile pictures (body ideal vs. no body), body ideal comments

(pro-ideal vs. anti-ideal), and group identification (high vs. low). A total of 501 participants completed the web-based experiment and passed all manipulation checks.

Participants viewed pictures and comments on the Facebook status page and were able to leave their own comment before moving on to the post-test instrument.

Results demonstrated no significant main effects for either profile pictures or comments on participants’ body image. However, those with low predispositional body satisfaction displayed significantly lower body satisfaction after viewing body ideal

iv images than those with relatively higher predispositional body satisfaction. In addition, participants’ comments were overwhelmingly in line with the body ideal norm. But, in support of the SIDE model, the group norm did significantly affect participants’ behavior.

Those exposed to anti-ideal comments were almost three times more likely to make anti- ideal comments than those exposed to pro-ideal comments. In contrast to recent SIDE model research, group identification played no role in either participants’ adherence to the norm or in the relationship between exposure to body ideal pictures and comments on participants’ body image. It was speculated that the CMC context of SNSs may have played a role in the lack of significant effects of group identification. Altogether, the findings from this study demonstrated the importance of continued body image research in SNSs as well as the applicability of the SIDE model in this newer CMC context. In addition, the findings illuminated potential of further research on body image that is guided by the SIDE model. Finally, the successful implantation of a novel web-based experimental design shows promise for similar research in future communication inquiries.

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Dedicated to my mom, Laura, for encouraging me to follow my heart and for removing all doubt about what is possible in life.

Dedicated to my future wife, Layne, for her understanding, her enthusiasm, her unwavering support, and for always giving me a reason to smile.

vi ACKNOWLEDGMENTS

I would like to first recognize my advisor, Dr. Sung-Yeon Park, for her exhaustive efforts in guiding me on my scholarly path. I am especially indebted to Dr. Park for her time and energy, her honesty, her thoughtful and kind nature, and her ability to push me more than I thought possible and then encourage me to go a little further. Dr. Park’s tireless commitment to the pursuit of excellence has been an inspiration over the past three years, and I am extremely fortunate to have had the opportunity to work with her.

I have enormous appreciation for my committee members, Dr. Gi Woong Yun and Dr. Srinivas Melkote, as well as my graduate faculty representative, Dr. Gary Heba, for serving on my committee. I am forever grateful for Dr. Yun’s insights during the writing of my dissertation, and for his tremendous patience and dedication in teaching me how to develop web-based experiments, a skill that I gained over many months that included countless late-night Skype discussions. I am also extremely privileged to have had Dr. Melkote as a committee member. His expertise in quantitative research methods was an invaluable asset during my time at Bowling Green State University. He played a great role in helping me to select the most appropriate method for my research topic.

Finally, I would like to thank Dr. Heba for not only ensuring a smooth dissertation process, but also for his insightful comments along the way.

I would like to thank Emily Anzicek, Matt Meier, Faith Yingling, and the Center of Excellence for their help in recruiting participants, as well as all of the instructors who graciously provided me with access to their students. Without your help, writing my dissertation would not have been possible. Your courtesy and understanding are qualities

I will strive to embrace throughout my academic career. In addition, I would like to

vii recognize the efforts of Erin Ziegelmeyer during the coding process. Ziegelmeyer’s commitment and enthusiasm toward this project were greatly appreciated.

My sincerest gratitude goes to the faculty members of the School of Media and

Communication at Bowling Green State University, who helped shape my understanding of the our diverse field. The School of Media and Communication saw potential in me three years ago during the application process, which is something that continues to motivate me everyday. A special thanks also goes to Dr. Terry Rentner, for her mentorship that started during my first year in the graduate program. My experience was also enhanced by my many colleagues in the program, especially Alex and Dave for their eagerness to collaborate on research projects, and for providing their unique and interesting perspectives.

Lastly, I must acknowledge my friends and family, both near and far, for helping me over these past few years. Without you, I would not have started or made it through this journey.

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TABLE OF CONTENTS

Page

CHAPTER 1: INTRODUCTION ...... 1

Objectives of the Study ...... 11

Organization of the Dissertation ...... 15

CHAPTER II: LITERATURE REVIEW ...... 16

Media Effects on Body Image ...... 16

Social Network Sites (SNSs) ...... 23

The SIDE Model Approach ...... 39

Research Questions and Hypotheses ...... 61

CHAPTER III. METHOD ...... 70

Research Design ...... 70

Sample ...... 73

Experimental Factors ...... 74

Dependent Measures ...... 77

Additional Variables of Interest ...... 79

Procedure ...... 80

Strategies for Data Analysis ...... 82

CHAPTER IV. RESULTS ...... 87

Preliminary Analyses ...... 87

Research Question and Hypotheses Testing ...... 92

Post Hoc Supplementary Analyses ...... 102

CHAPTER V. DISCUSSION ...... 105 ix

Objective 1: Body Image Research on SNSs ...... 106

Objective 2: The SIDE Model and SNSs Through Group Identification ...... 110

Objective 3: The SIDE Model, Group Norms, and Societal Norms ...... 113

Objective 4: Exploring Gender Differences ...... 117

Objective 5: Practical Implications ...... 118

Objective 6: Implementation of a New Experimental Method ...... 119

Post Hoc Supplementary Analysis ...... 121

Limitations ...... 123

Summary ...... 126

REFERENCES ...... 128

APPENDIX A. RESEARCH DESIGN & DIRECTION OF INFLUENCE ...... 159

APPENDIX B. STIMULUS CODE ...... 161

APPENDIX C. CODEBOOK—PARTICIPANTS’ COMMENTS ...... 169

APPENDIX D. HSRB APPROVAL FORM AND INFORMED CONSENT ...... 170

APPENDIX E. INSTRUMENTATION DOCUMENTS ...... 172

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LIST OF TABLES

Table Page

1 Participant Demographic Information………………………………………………….183

2 Manipulation Checks…………………………………………………………………...184

3 Overall Participant Comments—Social and Group Norm Conformity ...... ….185

4 Media Usage..…………………………………………………………………………..186

5 Motivations for Facebook Use………………………………………………………….188

6 Conditions/Stimulus Cells……………………………………………………………...189

7 Body Image…………………………………………………………………………..…190

8 Hypothesis 1. Profile Pictures and Body Image……………………………….………191

9 Hypothesis 2. Friends’ Comments and Body Image……………………………...……192

10 Hypothesis 3. Interaction Effects: Pictures and Comments on Body Image……...……193

11 Hypothesis 4. Interaction Effects: Predispositional Body Satisfaction and Pictures on

Body Image…………………………………………………………………………..…196

12 Hypothesis 5. Interaction Effects: Predispositional Body Satisfaction and Comments

on Body Image.…………………………………………………………………………199

13 Hypothesis 6. Interaction Effects: Group Identification and Pictures on Body Image…202

14 Hypothesis 7. Interaction Effects: Group Identification and Comments on Body

Image……………………………………………………………………………………205

15 Hypothesis 8. Profile Pictures on Participants’ Comments…………………………….208

16 Hypothesis 9. Friends’ Comments on Participants’ Comments…………………..……209

17 Hypothesis 10. Interaction Effects: Pictures and Comments on Participants’ Comments. …………………………………………………………………………………………..211

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18 Hypothesis 11. Interaction Effects: Group Identification and Comments on Participants’

Comments………………………………………………………………………………212

19 Research Question 12. Gender Differences: Pictures on Body Image…………………213

20 Research Question 13. Gender Differences: Comments on Body Image………………216

21 Research Question 14. Gender Differences: Pictures on Participants’ Comments…….219

22 Research Question 15. Gender Differences: Comments on Participants’ Comments….220

23 Post Hoc Supplementary Analyses—Correlations……………………………………..221

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CHAPTER 1: INTRODUCTION

Over the past few decades, the issue of body image has gained considerable attention in media research, particularly in the study of media effects (Levine & Harrison, 2009). By simply passing by a magazine display at the local newsstand, watching television, or viewing ads— whether print, television, or on the Internet, one cannot escape the prominence of the human body. However, not just any body is likely to be featured. Research has shown the majority of the human forms displayed in media have come to represent less than 5% of the actual population (Smolak, 1996). Such a discrepancy between the body ideal and the actual population has contributed to body image disturbances, including disordered eating behavior.

The problem has even inspired numerous media productions, including Kilbourne’s insightful commentary on the advertising industry’s effects on female body image in Killing Us Softly

(2000; 2010) and Slim Hopes (1995), and National Geographic’s Taboo: Beauty (2011) that featured a model’s painful and eventual losing battle with anorexia nervosa.

While research on female body image has dominated this area of study, a growing collection of research on male body image suggests that this phenomenon is not bound by gender.

Mishkind et al.’s (1986) The Embodiment of Masculinity provided an early look at male body image issues and the release of The Adonis Complex (Pope, Phillips, & Olivardia, 2000), a book that demonstrated the pervasiveness of negative body image and disordered eating in men, helped to spark interest in the subject from the mainstream media as well as the research community. Male body image has also inspired several documentaries, such as Bell’s (2008)

Bigger, Stronger, Faster: The Side Effects of Being American and Gaudio and Taplin’s (2005)

Shredded, that focus on the pressures for boys and men to become more muscular and lean through steroid use. 2

Whereas women in media have gotten thinner, men have become increasingly more lean and muscular with increasing emphasis on grooming and dress—all the while appearing not to care (Pope et al., 2000). Such claims have received empirical support. From the 1980s and forward, many content analyses on body portrayals in various media have consistently confirmed the presence of a thin ideal for women and a lean and muscular ideal for men. For instance, research has demonstrated that both Playboy (Garner et al., 1980) and Playgirl (Leit, Pope, &

Gray, 2001) centerfolds have become increasingly slender and muscular respectively. The shift in media portrayals, in turn, may engender serious mental, physical, and behavioral health consequences for those trying to match the body ideal (Hobza et al., 2007).

Indeed, research has shown that this shift toward increasingly idealized body images in the media today has had a tangible negative impact on both body image and eating behavior.

Survey studies have consistently demonstrated that exposure to media featuring the body ideal, from magazines and television to video games, contributes to lower body satisfaction and disordered eating behavior in women (for an overview, see Levine & Harrison, 2009). In addition, experiments have shown a causal relationship between media exposure and body image in women. That is, exposure to media depictions of the body ideal often led to significantly lower body satisfaction and even modified eating behavior (Groesz, Levine, Murnen, 2002; Harrison,

Taylor, & Marske, 2006)

Although the effects in men are yet to match those experienced by women, they are nevertheless present and growing. In fact, conservative estimates of body image disturbances are

10% in men boys and young men and 15-20% in girls and young women (Levine & Harrison,

2009). Disordered eating behaviors, estimated to affect 8-10% of adolescent girls and young women, are present in men as well. Especially, the gender gap is even smaller for binge eating 3 disorder that is known to have a 3:2 ratio between women and men (Levine & Harrison, 2009).

In addition to disordered eating behavior, body dissatisfaction in men can lead to excessive exercising, abuse of steroids and other potentially dangerous supplements, and plastic surgery.

According to Pompper, Soto, and Piel (2007), “The ‘body sculpting’ movement is the fastest- growing market for plastic surgeries—males having silicone implants in their chest and calves”

(p.528). Indeed, survey studies have revealed correlations between media exposure such as magazines, television, movies, advertisements, video games and action figures and various symptoms of body image disturbance (Barlett, Vowels, & Saucier, 2008). Experiments have demonstrated causality of some of the correlations (Barlett, et al., 2008).

Although media effect research on body image has advanced to examine the influence in various scopes and media contexts, the role of social network sites (SNSs) in body image disturbances remains relatively unexplored. Scholars believe that how we perceive our peers and ourselves has dramatically changed through the use of contemporary SNSs (Zywica & Danowski,

2008). Friedlander (2011), noting the connection between profile pictures on SNSs and traditional portraiture, suggests that even in the most basic portrait, a specific identity is being portrayed based on what characteristics the “sitter” wishes to reveal. The characteristics include physical appearance, mood, social and/or professional status, and possibly, even ideological commitment. Yet, personal portraits that used to belong to those in the upper echelons of society have now become common in digital form to hundreds of millions of people through SNS profile pictures. And while online social networks have been in existence for decades, they have recently reached levels of sophistication unparalleled to any past form. Technology has allowed for the creation of profiles online that include pictures, videos, background information, likes and interests, relationship status, public comments, private messages, and up-to-the-minute 4 geographic location, among many other things. So, while a picture is said to be worth a thousand words, words themselves serve to reinforce and expand SNS users’ understanding of the norms communicated through images, adding a social dimension to this process. Indeed, users can communicate through private messages and public posts, and some scholars argue that the comments exchanged by users may be as or even more influential than the profile pictures themselves (Walther, 2007; Walther & Parks, 2002).

The need to study the effects of SNSs on body image also arises from their popularity. For example, Facebook currently has over 850 million active members worldwide and is still growing. More than 50 percent of them log in everyday, and users spend over 700 million minutes per month logged in (Facebook statistics, 2012). This is just one SNS, but it is the most prominent one today in the U.S. Moreover, SNSs allow a unique space where we can and do create more attractive versions of ourselves. Those studying self-presentation on SNSs have consistently found that users alter their appearance in an attempt to meet societal beauty ideals

(Ellison, Heino, & Gibbs, 2006; Strano, 2008).

There is a growing body of research focusing on self-presentations on SNSs. It has been found that SNS users commonly manipulate their own online profiles to appear more popular and attractive (boyd, 2006; Donath & boyd, 2004; boyd, 2007; boyd and Ellison, 2007;

Papacharissi, 2002; Walther et al., 2008). Ironically, SNS users, many of whom engage in the manipulation themselves, tend not to realize that profile photos were shot from particular angles and edited to portray the most idealized self-representation possible (Strano, 2008). Then, it reasons that exposure to images and other self-representations on SNSs may affect the body image of users. Yet, there is a lack of attention to the effects of SNSs from a body image perspective. Thus, focused attention to the effects of SNS usage on body image is a logical step 5 to expand on our collective understanding of media influence on body image. As noted above, hundreds of millions of people log on to Facebook every day. If the body image of even 1% of daily users were negatively impacted, that number would be staggering.

The impact of SNS usage on one’s body image is an important issue for academic researchers to investigate. Recent studies have found that Facebook does in fact have potential to negatively affect one’s body satisfaction (Haferkamp & Kramer, 2011; Latzer, Katz, & Spivak,

2011). Two studies to date have examined this relationship using Festinger’s social comparison theory. In one study, Latzer, Katz, and Spivak (2011) investigated the relationship between media exposure and body image in adolescent girls. By asking respondents about their use of various media types—from traditional to online media—and their body perceptions, the authors found that time spent on Facebook was correlated with body dissatisfaction and eating disorder pathology. At the same time, the researchers also discovered that a sense of empowerment mediated these results. That is, if one’s sense of empowerment was high, the effects of the SNSs on body image were not as strong. The second study, an experiment conducted by Haferkamp and Kramer (2011), examined the effects of SNS profiles on body image. Results demonstrated that after exposure to attractive looking personal profiles, as opposed to unattractive looking profiles, participants had a more negative body image than after exposure to neutral/unattractive profiles.

So far, the significant connection between exposure to SNSs and body image concerns demonstrated in two separate studies, one survey and one experiment, provides initial support for the speculation that the usage of SNSs is harmful to body image. Moreover, the results show that both men and women may be subject to the effects. Although Latzer et al. (2011) examined young women only, it is possible that a similar effect is observed in young men as well. 6

Haferkamp and Kramer (2011) found no gender differences in the results from their experiment—viewing attractive profile pictures significantly influenced the body image of men and women. The authors asserted that, as opposed to fashion magazines, cosmetic advertisements, or modeling shows, SNSs offer a fairly equal opportunity for both men and women to view body ideals.

In the effect process, group identification may be an important, yet unexplored moderating variable with potential to influence the relationship between others’ self- presentations and one’s body image. That is, those who identify with a group that strongly adheres to the body ideal norm may be more adversely affected by their SNS usage. The social identity model of deindividuation effects (SIDE) (Reicher et al., 1995; Postmes & Spears, 1998) is a theoretical framework that provides support for this speculation. The SIDE model’s roots are grounded in social identity theory (Tajfel & Turner, 1979) and self-categorization theory

(Turner, 1991), both of which assume that one’s identity consists of both social and personal selves. The SIDE model states when there is a salient group identity (i.e. attractive self- presentation in SNSs), individuals tend to pay more attention to their group identity than their personal identity (Reicher et al., 1995; Postmes et al., 1998). The shift from the personal to the social self has been labeled deindividuation (Lea & Spears, 1991). When deindividuation occurs,

SIDE model predicts that individuals are more guided by group norms than by personal norms in their behavior (Reicher, 1984; Reicher et al., 1995).

This proposition was presented as an alternative to a more traditional view of classical deindividuation theory which maintained that, through deindividuation, individuals experience a loss of self and consequently display anti-normative behavior (Diener, 1977; Festinger, 1954;

Zimbardo, 1969). Arguably the most notable scholar holding the traditional view was Philip 7

Zimbardo whose Stanford prison experiments in 1971 garnered widespread recognition.

Zimbardo (1969) argued that when individuals do not possess individuating cues, they may experience a complete loss of self and engage in irrational behavior. However, a meta-analysis of over 60 deindividuation studies found little empirical support for this assumption and instead endorsed the SIDE perspective (Postmes & Spears, 1998). That is, when there is a lack of individuating cues, individuals shift from a personal identity to a salient group identity, and adhere to the perceived norms of the group.

The SIDE model has been used in both face-to-face and computer-mediated communication (CMC) contexts, but CMC has been the primary focus of most researchers investigating the theoretical framework. For instance, the model has been used to explain human behavior though email (Chan, 2010), in small group online discussion (Hogg & Tindale, 2005;

Spears et al., 2002), and, more recently, in YouTube comments (Walther et al., 2010) and other virtual environments (Lee, 2007a; 2007b). Some research endeavors have also used the SIDE model to address practical issues, such as soliciting donations for a church cause (Chan, 2010), engaging in collective protest (Spears et al., 2002), and maximizing the effects of a PSA posted on YouTube (Walther et al., 2010).

In addition to refuting the traditional deindividuation theory in offline contexts, the SIDE model has been used to explain irrational behaviors online, of which “flaming” received the most attention from communication scholars. Prior to the application of the SIDE model, most flaming research followed the argument posited by Kiesler, Zubrow, Moses, and Geller (1985) who employed the traditional deindividuation approach. They maintained that individuals tend to be more uninhibited when communicating in computer-mediated settings due to the lack of social cues and that this disinhibition is more likely to create an environment where flaming 8 occurs more often. However, this view was soon labeled technological determinist in that the medium was believed to foster a sort of blanket effect on users’ behavior. Instead, some scholars adopted the SIDE model perspective and suggested that behavior online is actually highly normative, and that flaming behavior is a product of group norms, not irrational behavior

(O’Sullivan & Flanagin, 2003; Walther; 1994). Though not directly related to the current investigation, the findings on flaming behavior online serve to further demonstrate the contribution the SIDE model has had on CMC research seeking to understand behavior of

Internet users.

The SIDE model has evolved extensively since its initial conceptualization. Two developments are of particular relevance to the current research that explores the effects of SNS profile pictures and comments on body image are: 1) the expansion to include a strategic component as well as a cognitive component; and 2) the recent findings that group salience/identification may be a more important factor than anonymity in the deindividuation process, which renders the SIDE model applicable in nonymous CMC contexts like SNSs.

The strategic component of the SIDE model shows the model’s adaptability to explain a variety of group behavior and draws a connection between the SIDE model and the self- presentation perspective. SIDE model research initially focused on what was labeled the

“cognitive aspect” of the model. In other words, the emphases were on the internal processes individuals engage in when perceiving the saliency of the group, and whether they adopt a social identity over their personal identity. Indeed, research in this area has consistently found that, when deindividuated (typically manipulated through anonymity), individuals tend to identify with a salient group and thus adopt the norms of that group (Postmes & Spears, 1998; Reicher,

1984; Spears et al., 1990; Walther et al., 2010) 9

However, more recent installations of the theory have addressed the strategic aspect of the model which focuses on intentional behavior individuals engage in as they navigate their group membership. The strategic component has been mostly explored in research on self- presentation. Studies have shown that, when in CMC environments, individuals make strategic decisions about their self-presentation based on anticipated social sanctions from others (Barreto

& Ellemers, 2000; Ellemers et al., 2000; Tanis & Postmes, 2003). In fact, in a recent theoretical analysis of the SIDE model, the term “social identity performance” was used in place of the

“strategic component” to highlight the intentional and performative nature of activities individuals engage in to manage their social identities (Klein, Spears, & Reicher, 2007). The link between the SIDE model and self-presentation has paved the way for the use of the SIDE model to explain normative group behavior in more nonymous CMC environments like SNSs. A few studies have already looked at the way individuals present themselves through visual and verbal cues. As noted above, these investigations have found that individuals consistently adapt their self-presentations to the societal norms of attractiveness and beauty (Ellison, Heino, & Gibbs,

2006; boyd, 2008; Zhao, Grasmuck, & Martin, 2008; Papacharissi, 2002). The SIDE perspective may provide insight into the mechanisms behind such self-presentation processes online.

The second development in SIDE model research, that the deindividuation process may be more dependent on group identification than anonymity allows the model’s use in the study of nonymous CMC contexts like SNSs. Perhaps delay in the adoption of the SIDE model in SNSs has been at least partially due to its traditional focus on anonymity as a trigger of deindividuation.

From the start, typical SIDE experiments utilized anonymity to investigate adherence to group norms. Researchers consistently found that in anonymous conditions, individuals were less able to pick out individuating information about others with whom they were communicating and 10 thus more likely to focus on group norms (Postmes & Spears, 1998). However, this process was later found to be mediated by group identification: When group identification was low, individuals were less likely to deindividuate, even in anonymous conditions (Barreto & Ellemers,

2000; Ellemers et al., 2000; Lee, 2006; Terry and Hogg, 2001). As the theory has evolved, scholars have found group salience to be the most important factor prompting individuals to adhere to a social identity. This has been particularly true when scholars have investigated the strategic component of the SIDE model. For example, an experiment demonstrated that “the degree of identification with the group determines how group members respond to the social context” (Barreto & Ellemers, 2000, p. 903). The authors found that high group identifiers adhered to norms whether or not there was the threat of social sanction, while low group identifiers only did so when held accountable for their behavior (Barreto & Ellemers, 2000).

In the current study, a more concrete connection between the SIDE model and body image research is investigated. If the body norm of a SNS group one identifies with is highly idealized, high identifiers with the SNS group may experience negative body image as a consequence of trying to meet the norm. In other words, if the highly idealized body norm in a

Facebook group is made salient through friends’ profile pictures and comments that support the norm, individuals may feel greater pressure to adhere to the norm, and possibly, experience greater negative effects on their body satisfaction. The potential effects are not limited to high identifiers. Low identifiers may also feel the pressures of an unrealistic body ideal norm on

SNSs, and experience negative body image if they perceive social sanctions for not meeting the body ideal norm from a SNS group to which they belong, although possibly to a lesser extent.

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Objectives of the Study

In light of the issues discussed here, the following research objectives have been formulated. First, this research attempts to expand the breadth of body image scholarship by investigating media effects of a relatively new medium—SNSs. Second, this study tries to broaden the scope of the SIDE model by testing the model using explicit visual and verbal information on the SNS Facebook—a CMC environment that has yet to be examined from this perspective. Third, although the SIDE model has tested the power of categorical level norms like gender and race to influence behavior, this research attempts to demonstrate that the consistent reinforcement of the body ideal has elevated it to the status of a cultural norm with the ability to affect behavior. Fourth, this investigation will determine if the effects are different by the gender of participants. Fifth, this study tries to gain insight for practical strategies that may be useful to those interested in ameliorating potentially negative effects of SNS usage on body image. Sixth, this research strives to develop a new experimental method that more closely resembles the actual experience of using the SNSs. Based on these broad objectives, the following research questions and hypotheses have been posited.

RQ1: What are the effects of exposure to body ideal profile pictures on Facebook on

users’ body image?

H1. Exposure to body ideal profile pictures will negatively impact users’ body image.

RQ2: What are the effects of exposure to body ideal comments on Facebook on users’

body image?

H2. Exposure to body ideal comments will negatively impact users’ body image. 12

RQ3: Is there an interaction effect between Facebook body ideal profile pictures and

body ideal comments on users’ body image?

H3. Users exposed to body ideal profile pictures and body ideal comments will exhibit

the most negative body image.

RQ4: Does participants’ predispositional body satisfaction affect the relationship

between exposure to body ideal profile pictures on Facebook and body image?

H4. Participants’ predispositional body satisfaction will moderate the relationship

between exposure to SNS body ideal profile pictures and body image in that those with

lower baseline body satisfaction will exhibit greater negative effects on their body image.

RQ5: Does participants’ predispositional body satisfaction affect the relationship

between exposure to body ideal comments on Facebook and body image?

H5. Participants’ predispositional body satisfaction will moderate the relationship

between exposure to body ideal comments and body image in that those with lower

baseline body satisfaction will exhibit greater negative effects on their body image.

RQ6: Does group identification affect the relationship between exposure to body ideal

profile pictures on Facebook and body image?

H6. Group identification will moderate the relationship between exposure to body ideal

profile pictures and body image in that stronger group identification will result in more

negative body image. 13

RQ7: Does group identification affect the relationship between exposure to body ideal

comments on Facebook and body image?

H7. Group identification will moderate the relationship between exposure to body ideal

comments and body image in that stronger group identification will result in more

negative body image.

RQ8: Does exposure to body ideal profile pictures on Facebook affect participants’ body

ideal norm comments?

H8. Exposure to body ideal profile pictures will prompt participants to make pro-ideal

comments.

RQ9: Does exposure to body ideal comments on Facebook affect participants’ body

ideal norm comments?

H9. Exposure to body ideal comments will prompt participants to make pro-ideal

comments.

RQ10: Is there an interaction effect between body ideal profile pictures on Facebook and

body ideal comments on participants’ body ideal norm comments?

H10. Participants exposed to body ideal profile pictures and body ideal comments will

make pro-ideal comments the most frequently.

14

RQ11: Does group identification affect the relationship between body ideal comments on

Facebook and participants’ body ideal norm comments?

H11. Group identification will moderate the relationship between body ideal comments

and participants’ pro-ideal comments in that stronger group identification will result in

greater adherence to the body ideal norm.

RQ12: Are the effects of Facebook body ideal profile pictures on body image different

between men and women?

RQ13: Are the effects of Facebook body ideal comments on body image different

between men and women?

RQ14: Are the effects of Facebook body ideal profile pictures on participants’ pro-ideal

comments different between men and women?

RQ15: Are the effects of Facebook body ideal comments on participants’ pro-ideal

comments different between men and women?

15

Organization of the Dissertation

Chapter 2 explores the relevant literature and the theoretical framework of this study.

Literature reviewed can be grouped in three key areas: media effects on body image, SNSs, and the SIDE model. Chapter 3 provides an overview of the method chosen for this study, followed by specific details of the experiment, of all measures used, a description of the population and sample, and data collection and analysis procedure. Chapter 4 contains the results from data analysis and answers the research questions and hypotheses. Chapter 5 discusses the results, draws conclusions based on the findings, and proposes directions for future research by reflecting on the limitations of the study.

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CHAPTER 2: LITERATURE REVIEW

Media Effects on Body Image

Representations of Body Image

Investigations of the body ideal in the media through content analysis started to build up in the 1980s, demonstrating the need for more systematic analysis of the relationship between media representations of body ideals and their impact on audiences. Initially, content analyses on body image focused almost exclusively on media presentations of the female body. The studies collectively revealed that, since as early as the 1960s, media portrayals of the female body have become increasingly thinner (Beasley & Collins, Standley, 2002; Dietz, 1998; Dill &

Thill, 2007; Garner, Garfinkel, Schwartz, & Thompson, 1980; Mazur, 1986; Silverstein, Peterson,

& Perdue, 1986; Wiseman, Gray, Mosimann, & Ahrens, 1992). Also, the thin female body ideal has been common in multiple domains of beauty, including but not limited to actresses

(Silverstein et al., 1986), Miss America contestants, Playboy centerfolds (Garner et al., 1980), and video game characters (Beasley & Collins Standley, 2002; Dietz, 1998; Dill & Thill, 2007).

Moreover, this shift has occurred while American women have gained weight (Garner et al.,

1980; Greenberg et al., 2003; Silverstein, Perdue, Peterson, & Kelly, 1986; Spitzer, Henderson,

& Zivian, 1999). Unfortunately, most of the weight gain turned out to have come from fat

(Kuczmarski et al., 1994; Spitzer et al., 1999). As a consequence, there now exists an ever-large gap between the female population’s actual bodies and media portrayals of the body ideal.

Parallel to these investigations on women’s body image, scholars began asking if the same phenomenon was happening in men, and examinations of portrayals of male bodies in the media increased considerably in the late 90s and early 2000s (Frederick, Fessler, & Haselton,

2005; Labre, 2005; Law & Labre, 2002; Leit, Pope, & Gray, 2001; Pope, Olivardia, Gruber, 17

Borowiecki, 1999). However, researchers soon came to realize that the focus on thinness that has been typical for female body image research was not appropriate for the examination of male body image (Pope et al., 1999; Schooler & Ward, 2006). The male body as portrayed in the media has become progressively more V-shaped over the past several decades, which indicates increased leanness in conjunction with more pronounced muscularity (Pope et al., 2000). In fact, changes in the male body ideal have become so dramatic that, many experts agree, the current ideal may not be attainable without the aid of controlled substances such as steroids (Pope et al.,

1999). Like in the analyses of female images, these changes were also found in multiple media domains such as Playgirl centerfolds (Leit et al., 2001), action figures (Pope et al., 1999), models

(Spitzer et al., 1999; Lin, 1998; Labre, 2005), and video game characters (Dill & Thill, 2007;

Scharrer, 2004). Not only has the male body image changed significantly over the years, but the

American population of men, like women, has gained fat (Kuczmarski et al., 1994; Spitzer et al.,

1999). Therefore, a similar gap now exists between the population of men and the male ideal presented in the media.

Other than the gender difference in the conceptualization of the body ideal, Lin (1998) highlighted another difference between media portrayals of the ideal body for men and women:

The female ideal was more saliently presented in the media than the male ideal. Women were featured in revealing clothing and sexually suggestive ways more often than men. Furthermore,

Greenberg et al. (2003) found, the body ideal appears to be more strictly applied in the media for women than for men. Although the majority of men and women in the U.S. were overweight or obese, only 13% of women were overweight or obese in television portrayals, compared to 24% of men. However, when it comes to the media that cater to gender-specific target audiences, the gender differences may not be as pronounced. For example, Andersen and DiDomenico (1992) 18 found that even though there was significantly more diet-focused content in female-audience magazines, there were significantly more exercise and weightlifting content in male-audience magazines.

Not only are the media images promoting unrealistic body ideals for both men and women, but these images have also been linked to body dissatisfaction and risky weight-management behaviors to meet the ideals. The relationship between media exposure and various symptoms of body image disturbances and disordered eating has been well established through surveys and experiments (Levine & Harrison, 2009).

The Impact of Media Representations: Surveys

Shortly after the surge of content analyses on media images of the female body, survey studies began to emerge in academic journals. In these analyses, media effects on women’s body image were examined at two levels—attitudes about one’s body image (e.g., body esteem, body dissatisfaction) and behaviors (e.g., disordered eating behavior, exercise, diet-pill/laxative abuse), including behavioral intensions to engage in the behaviors. Early on, Stice and colleagues

(1994) tested this relationship through structural equation modeling and found that media exposure, as mediated by ideal body stereotype internalization and body dissatisfaction, is linked to eating disorder symptomology. Since then, many others have found a significant relationship between media exposure and body image disturbances in women (e.g., Botta, 1999; Harrison,

1997; Harrison & Cantor, 1997; Levine, Smolak, & Hayden, 1994; Martin & Kennedy, 1993;

Stice, Schupak-Neuberg, Shaw, & Stein, 1994; Thompson, Heinberg, Altabe, & Tantleff-Dunn,

1999). Moreover, these results have been found for a variety of traditional media, including television, magazines, movies, and video games. Lastly, the media effects on body image disturbances have been found in college-aged women and young adults (Harrison & Cantor, 19

1997; Stice et al., 1994), adolescents (Botta, 2003), and grade-school girls (Harrison, 2001;

Levine et al, 1994; Martin & Kennedy, 1993), showing that the negative psychological effects of media on body image can start at an alarmingly early age (Harrison, 2000).

Though lagging behind the studies focusing on female body image, studies exploring the relationship between media exposure and male body image began to accumulate (Botta, 2003;

Murnen, Smolak, Mills, & Good, 2003; Schooler & Ward, 2006; Smolak, Murnen, & Thompson,

2005; Stanford & McCabe, 2005). Parallel to the female body image studies, media effects on men’s body image can also be categorized as either attitudinal or behavioral. Another similarity between men and women is that the effects of media exposure have been found for an array of media types, including television, magazines, movies, and video games. One final gender similarity is that these effects have been seen in college-aged men, adolescents (Harrison, 2001;

Smolak et al., 2005; Stanford & McCabe, 2005) and in boys as young as 4th graders (Murnen et al., 2003; Harrison, 2000).

Still, there is one aspect on which gender difference in the media effects on body image is consistently found: the magnitude of effects. Studies that measured the effects of media on body image in samples of both men and women have found significant gender differences (Harrison,

2000; 2001; Harrison & Cantor, 1997; Heinberg & Thompson, 1992; McCreary, & Sadava,

1999; Murnen et al., 2003). According to these studies, women exhibited significantly higher levels of body dissatisfaction and weight-management behavior to change one’s body image than men. However, results from Cash’s (1997) national survey suggest that the gender difference in body dissatisfaction may be exaggerated. When compared to earlier reports (Berscheid, Walster,

& Bohrnstedt, 1973; Cash, Winstead, & Janda, 1986), Cash found an increase in body dissatisfaction for both genders. The percentage of men with body dissatisfaction changed from 20

15% to 43%, whereas the percentage of change for women was from 23% to 56%. In addition,

Botta (2003) found that boys and girls engaged in similar social comparison practices and experienced desire for thinness and bulimic behavior at comparable levels. The author pointed to the ten-year gap between her findings and previous work (Heinberg & Thompson, 1992) that found such effects primarily in girls. Overall, the available research suggests that men are paying more attention to body ideals than in the past and suffering similar psychological repercussions as those experienced by women (Agliata and Tantleff-Dunn, 2004; Botta, 2003).

There are some limitations of the survey studies that examined the relationship between media exposure and body image disturbance of women and men. The first, as discussed by

Smolak, Murnen and Thompson (2005), is that many studies used confounding measures of media influence that may have affected the outcomes of the research. The authors insisted that media exposure and internalization of the thin ideal are different concepts and thus deserve separate measures. Second, most surveys employed a cross-sectional design, which limited their explanatory power. However, two longitudinal studies do exist. In a longitudinal cohort study of 9-14 year-old girls, Field et al. (1999) found that the desire to be thin and wanting to look like women in TV and magazines were significantly related to purging behavior. In another study, exposure to video game magazines increased young boys’ drive for muscularity over time

(Harrison & Bond, 2007). A third limitation, one true of all surveys, is a lack of internal validity, or the inability to establish a causal relationship (Singleton & Straits, 2005). Harrison (2001) noted this limitation in her article and called for more experiments that could strengthen the causal link between media consumption and body image disturbances.

The Impact of Media Representations: Experiments 21

As with the content analyses and surveys reviewed above, most early experiments investigating media effects on body image used all female samples. Also, like surveys, most experiments have found significant relationships between media exposure and attitudes toward one’s body and behavioral intent to engage in body control practices (Heinberg & Thompson,

1995; Henderson-King, Henderson-King, & Hoffman, 2001; Irving, 1990; Posavac, Posavac, &

Posavac, 1998; Richins, 1991; Stice and Shaw, 1994; Stice, Spangler, & Agras, 2001; Thornton,

& Maurice, 1997). However, the experiments have the added ability to demonstrate a causal link between these factors. There were several other consistencies across the experiments with female-only samples. One, a majority of the experiments found baseline body satisfaction (or some variant of this) to moderate the relationship between media exposure and body image disturbance (Henderson-King & Hoffman, 2001; Posavac et al., 1998; Stice et al., 2001; Stice &

Shaw, 1994). That is, those with higher levels of initial body dissatisfaction were more likely to be affected by images of the thin ideal. Second, the experiments used a variety of media types as manipulation, including images of thin models, magazines, and television. Lastly, most of them employed self-reports to measure body image disturbances.

Research that employed an experimental method to investigate men’s body image gained momentum at the turn of the century. Similar to women’s body image studies, media exposure was repeatedly shown to affect men’s body satisfaction and behavior to alter one’s body (Agliata

& Tantleff-Dunn, 2004; Halliwell, Dittmar, & Osborn, 2007; Hobza, Walker, Yakushko, &

Peugh, 2007; Aubrey & Taylor, 2009; Arbour & Ginis, 2006; Leit, Gray, & Pope, 2002;

Lorenzen, Greive, & Thomas, 2004). However, instead of thinness that was the ultimate outcome pursued by women as a consequence of media exposure, it was muscularity that men were preoccupied with. Because of this, the common stimuli for experiments with men have 22 been media images of muscular ideals, whereas women have been typically exposed to thin ideals. Other than this, there were several similarities between male-only and female-only experiments. First, male-only experiments used multiple media types for manipulation, including images of muscular models, health, fitness, and fashion magazines, and television programs. Second, many of the studies found participants’ predispositions, such as body dissatisfaction and body shaping efforts (e.g. gym users vs. nonusers), to moderate the effects of media exposure on men’s body image (Agliata & Tantleff-Dunn, 2004; Arbour & Ginis, 2006;

Aubrey & Taylor, 2009; Halliwell et al., 2007). Third, the investigations noted here used self- reports to measure body image disturbances.

Although several gender similarities exist in the experiments, it was initially speculated that men were more resistant to the body ideal than women. In fact, Kalodner (1997) found that images of the body ideal affected women’s self-consciousness and anxiety, but found no effects for men. However, Kalodner’s results seem to be the exception. Most experiments that examined both women and men found significant media effects in both groups (Grogan,

Williams, & Conner, 1996; Lavine, Sweeney, & Wagner, 1999; Ogden & Mundray, 1996). The main difference was that women viewed their bodies as too large, whereas men considered them as too small (Harrison, Taylor, & Marske, 2006). Measuring actual eating behavior, Harrison,

Taylor, and Marske (2006) found that women ate less in front of other women while men ate more in front of other men after being exposed to ideal images and text. This finding further supports that although both men and women are affected by media body ideals, manifestations of the symptoms are strikingly different. Also, while most investigations used self-reports,

Harrison and colleagues measured actual eating behavior after exposure to media images of body ideals. 23

One limitation commonly found in many of the experimental studies was the usage of short-term exposure manipulations. Still, one study did find significant effects through prolonged exposure in a longitudinal experiment (Stice, Spangler, & Agras, 2001). Stice,

Spangler, and Agras (2001) reported that adolescent girls with low baseline body satisfaction exhibited even lower body satisfaction after a 15-month subscription to a fashion magazine.

More longitudinal investigations would serve to further strengthen the argument that media exposure causes serious and sustained danger to the body image of women and men.

Overall, there is a substantial body of research demonstrating that media portrayals of body ideals affect the body image of those exposed. From the 1980s through the 2000s, findings from analyses using the three primary quantitative methods have consistently corroborated with one another. However, scholars have predominantly studied body image portrayals and effects in traditional media contexts and they merely scratched the surface in investigating the effects of the use of new media like SNSs. This newer medium supports a combination of different types of communication, such as visual and text-based communication in multiple directions with strong potential for group identification among its users. Exploring the effects of SNSs usage on one’s body image is a logical next step and deserves close scrutiny.

Social Network Sites (SNSs)

Definition and Features

Social network sites have been defined in a variety of ways, and scholars are still debating to this date whether SNS is the label that best describes the medium. For instance, some used the term Web 2.0 because it is seemingly more encompassing (Walther et al., 2011), while others found SNSs more descriptive (boyd, 2007). Levinson (2009) used the term “new new media” instead of SNSs. Regardless of the label, there is some agreement on a few basic 24 defining characteristics. boyd and Ellison (2007), in their comprehensive review, defined SNSs by three characteristics. First, within each unique SNS, users are able to create a profile. Second, users are able to align themselves to others on the site by adding others to a list of “friends”.

Third, users are able to navigate their friend lists, view each friend’s profile page, and thus see their friends’ friends, which may lead users to expand their friend networks by adding more users to their friend list. It needs to be noted, however, it is not always possible for individual users anymore to see the friend list of other users or even friends due to more advanced privacy settings.

There has also been some debate about the use of the term “network” versus “networking” to define SNSs. Of the major arguments, most compelling is boyd and Ellison’s (2007) point that “networking” typically refers to the beginning of an interaction between two individuals who do not know each other. However, Beer (2008) does challenge that the term social network site is overly broad, including much more than the SNSs discussed in the current analysis, such as wikis and mashups, and advocated the use of social networking site instead. Because of the rationale provided by boyd and Ellison (2007) that individuals typically know each other in some capacity offline before becoming “friends” online, the current analysis adheres to the moniker

“social network site.”

Today, the term social network site has been stretched across multiple categories and platforms. For instance, many of the features discussed here are present in SNSs that are primarily designed for media sharing (YouTube, Flickr, Spotify, etc.…) or discovery (Stumble

Upon, Tumbl.in, etc..) as well (boyd, 2007). However, in the current study, the term SNSs is used to describe sites that function primarily as tools for connection between friends. Currently, 25

Facebook is the most popular of this type of SNSs with over 850 million active users (Facebook,

2012).

Although the actual term used to define SNSs has been debated, scholars have become, perhaps by virtue of having the aforementioned debate, fairly adept at describing the features of common SNSs. The most basic feature of SNSs is the user profile. Profiles are a form of individual homepage on SNSs (Buffardi & Campbell, 2008), and range widely in design and scope. Because SNSs evolved from dating sites (boyd, 2007), however, most include similar content such as a brief biography that includes likes, hobbies, interests, and others, a profile picture, and various user-generated and/or shared content (e.g., images, videos, links, etc.).

Second, profiles also typically feature a list of “friends,” “contacts,” or “fans,” other members of the site who are observably linked to a specific profile owner (Beer, 2008; boyd & Ellison, 2007).

A list of friends is available for other users to see, although as noted previously, privacy settings allow users to control this among many other features. Third, user profiles typically display a space for other members to leave comments. This space has been labeled various names, but on

Facebook, the most popular SNS today, it is called a users’ “wall”. On the wall, users and their friends can create and respond to posts. By design, the most recent post or response to an earlier post is elevated to the top of one’s wall.

A Brief History

Although SNSs have existed for more than a decade, a coherent body of literature documenting their evolution is lacking. However, boyd and Ellison’s (2007) article, Social network sites: Definition, history, and scholarship, does provide a comprehensive history of

SNSs. Curiously, not many, if any, other scholars have published such a detailed evolution of

SNSs. This may be perhaps because many SNSs’ existence is so fluid: Some sites are born and 26 die almost too quickly to notice; Others start hot and then cool, lying seemingly dormant and yet still available for years; Still others have come to such a prominence that they have reshaped the very way we interact. Cataloging all of this is truly a daunting task.

Arguably, SNSs as we know them today began in 1997 with the creation of

SixDegrees.com. SixDegrees was one of the first sites that allowed users to build profile pages and create friend lists. During this early period, or adolescence of SNSs (Nickson, January,

2009), several other smaller ethnically targeted sites were developed, including

AsianAvenue.com in 1997, BlackPlanet.com in 1999, and MiGente.com in 2000, geared toward the Latino community. These sites are all currently active, and as of 2008, BlackPlanet was the most popular multicultural SNS and the fourth most visited SNS overall, with over 20 million users (BlackPlanet.com).

However, it was not until 2002 with the advent of Friendster that SNSs gained social prominence. Friendster started as a dating site, but quickly became a place to network with friends as well. Music bands also started using Friendster to promote themselves, but this practice was not permitted (boyd, 2006). As an answer to solve this problem, MySpace was launched in 2003 and thoroughly embraced its role as a site of active promotion for musicians, and, later, other artists and entertainers. boyd (2007) noted that the focus on music led to the popularity of Myspace beyond the bands themselves: “Given the degree to which youth are active participants in music subcultures, it is not surprising that MySpace attracted young fans”

(p. 4). Soon after the boom of Myspace’s popularity, LinkedIn, a professionally focused SNS was created. Although LinkedIn shared many of the features of Myspace including a brief biography, profile picture, and friend comment page, LinkedIn was specifically designed to 27 create professional networks. For this reason, LinkedIn highlighted background information related to education and work history, and also allowed users to upload resumes and references.

Following the early success of sites like Friendster and Myspace in 2002 and 2003, many new sites were created. Most focused on the features of sites that had previously reached success, such as Friendster (boyd & Ellison, 2007). Many of these sites were attracting large and nation- specific populations worldwide, such as Orkut in Brazil, Mixi in Japan, Lunarstorm in Sweden,

Hyves in Holland, Grono in Poland and Bebo in England, New Zealand, and Australia (boyd &

Ellison, 2007).

In 2004, Facebook was launched with a relatively narrow audience of college students in mind, but soon expanded rapidly. At first, only individuals with a Harvard.edu email account were permitted to join the network. Shortly after, however, all individuals with an .edu account were allowed to join, and eventually, in 2005, Facebook opened the network to anyone who desired membership. Facebook’s focus on heightened privacy constraints and a sense of exclusivity helped lift it above the popularity of Myspace, a site where privacy and safety issues such as the presence of sexual predators were increasing by 2006 (Gordon, 2006). Although

Facebook has also had similar safety issues as demonstrated in the 2007 New York State investigation into Facebook’s safety regulations to evaluate Facebook’s claims that the site is safe for high school and middle school students (Barnard, 2007), Facebook has become the most visited SNS with over 850 million active users by early 2012.

While Facebook is the juggernaut of SNSs today, two sites have been growing more rapidly over the past two years. Twitter, a microblog launched in 2006, allows users to create user profiles, post and follow small (140 characters or less) blog entries on one’s profile page, and connect with others such as friends, businesses, celebrities, activist groups, and many others. 28

On Twitter, connections between others work in two ways: You can choose to “follow” another user and thus access their Tweets or vice versa. While Facebook’s growth has slowed in the past few years, Twitter has continued to gain new members in large numbers, currently exceeding

400 million users worldwide (Twitter.com, 2012). Another recent SNS with exceptional growth is Pinterest, a site that focuses on creating and sharing pin boards—a sort of electronic scrap board—with other interested Pinterest users. This site was the fastest SNS to reach 10 million members in the history of SNSs and is overwhelmingly comprised of female users who account for over 80% of its user base (Pinterest.com, 2012).

In addition, there has been a recent movement by many to smaller, niche social network sites like Path, FamilyLeaf, and Pair (Stross, 2012). These newer sites have strict friend limits—

Path is set at 150, FamilyLeaf is for family connections only, and Pair is a SNS between two individuals—whereas Facebook does not currently have a limit in the number of friends.

According to a recent Pew study, the average Facebook user has about 245 friends (Pew Internet and American Life Project, 2012).

Research on SNSs

According to boyd and Ellison (2007), the research on SNSs has primarily focused on these four main areas: impression management and identity performance, networks and network structure, online/offline connections, and privacy issues. Of course, not all research on SNSs fits neatly within these boundaries. For example, one recent investigation explored the pedagogical use for Facebook (Watermeyer, 2010). In addition, Beer (2008) called for a critical approach that focuses on the capitalist framework in which SNSs operate. However, the four aforementioned areas collectively have received the most research attention in recent years

(Hargittai & Hsieh, 2011). 29

First, research on impression management and identity performance has focused heavily on Goffman’s (1959) The presentation of Self in Everyday Life to describe identity formation on

SNSs (boyd, 2007; Papacharissi, 2002; Tufekci, 2008; Zywica & Danowski, 2008). Goffman asserted that the presentation of self is an ongoing process of impression management. This process consists of performance, interpretation, and adjustment (Goffman, 1959). First, the performance is when individuals attempt to create desired impressions. Second, individuals notice how others interpret their impressions, which may be different from individuals’ original intentions. Finally, individuals make adjustments to their performance to better convey their intended impression.

Notably, in the performance of impression management, Goffman (1959) differentiated between expressions that individuals give and expressions that are given off. Those that one gives are self-created bits of information that one wishes to convey to others. These expressions are much easier to control and design than expressions that are given off (Goffman, 1959).

Expressions that are given off are typically nonverbal and unintentional (Papacharissi, 2002). To further distinguishing between these two types of expressions, scholars have noted that expressions one gives are explicit, such as the information section of a profile page, whereas expressions that one gives off are implicit, and conveyed through visual appearance (Goffman,

1959; Strano, 2008). Interestingly, the online space of SNSs allows users to control both types of expressions in everything from carefully constructed profile pages, the inclusion (or exclusion) of personal information, editing profile pictures, to well-thought comments, posts, and updates.

One recent study found Facebook users to exert greater effort toward making implicit expressions versus explicit expressions (Zhao, Grasmuck, & Martin, 2008). The authors found the average Facebook user to have uploaded about 85 pictures. 30

Exploring social networks on SNSs is a second area that has received some research attention (Barabasi, 2003; Donath, 2007; Donath & boyd, 2004; Heer & boyd, 2005; Paolillo &

Wright, 2005; Papacharissi, 2011). This research focuses on individuals as nodes that are connected to others through various links. Individuals with more links may be considered communication “hubs” in that information typically passes through them and then to their extensive networks of nodes. Hubs are also considered to have tremendous reach because they are so well connected (Barabasi, 2003). Through network analysis, researchers are able to see the vast and complex social networks that exist on SNSs. For example, researchers have explored the nuances of trust within social networks, demonstrating that an effective way to get useful knowledge (e.g., non-redundant information) is through weak ties that are trusted (Levin

& Cross, 2004).

Others have analyzed different network structures on SNS. Researchers have shown that friends exist in different capacities, such as passive members, inviters, and linkers (Kumar,

Novak, & Tomkins, 2006) or dabblers, devotees, samplers, and omnivores (Hargittai & Hsieh,

2011). Also, investigations have explored the influence of geographic location on friendship

(Liben-Nowell, Novak, Kumar, Raghavan, & Tomkins, 2005). In addition, McPherson, Smith-

Lovin, and Cook (2001) suggested that how individuals are perceived is largely based on the characteristics of their friend networks. The network structure of SNS platforms themselves has also been examined, from open (e.g., Facebook), business (e.g., LinkedIn), and members-only platforms (e.g., ASmallWorld) (Papacharissi, 2009).

Research on networks has also begun to gain traction in the communication discipline.

For example, Papacharissi’s (2011) recent book provided a collection of individually authored chapters that explored online social networks formed on SNSs from the communication theory 31 perspective. The authors explored social networks through multiple lenses, such as peer interaction, building social capital, and political and civic engagement.

Third, many studies on SNSs have focused on online/offline communication between individuals within social networks (Boase, Horrigan, Wellman, & Rainie, 2006; boyd, 2008;

Ellison, Steinfeld, & Lampe, 2007; Hampton & Wellman, 2003; Kavanaugh, Carroll, Rosson,

Zin, & Reese, 2005; Lampe, Ellison, & Steinfeld, 2006; Lenhart & Madden, 2006; Resnick,

2001; Wellman, Haase, Witte, & Hampton, 2001; Williams, 2006). Investigations of early CMC environments suggested that users were initiating relationships online and then potentially transitioning the relationship to encompass offline interaction as well (Wellman, Salaff,

Dimitrova, Garton, Gulia, & Haythornthwaite, 1996; Parks & Floyd, 1996). In other words, members of online social networks were more likely to meet online and then decide to meet in person. However, most data supports the contrary, that offline connections are followed by online connections (boyd & Ellison, 2007). Most recently, Waechter and colleagues (2010) weighed in the seeming contradiction by elaborating that the CMC platform plays a role in whether interaction begins online or offline. They noted that there is a difference between chat room interaction and communication via modern SNSs such as Facebook. When visiting chatrooms, individuals typically interact with people they do not know offline whereas, when on

SNSs, the opposite is true more often than not. Indeed, research suggests that SNS users typically know most of the individuals in their online social networks from prior face-to-face encounters (Lampe et al., 2006). A recent Pew study found that adolescents most commonly

(91%) used SNSs to connect with friends that they see often offline (Lenhart & Madden, 2007).

In addition to exploring the origins of social network interactions, whether online or offline, researchers have also explored the potential benefits of using SNSs. Specifically, studies 32 have focused on social capital gained from spending time on SNSs (Ahn, 2012; Ellison et al.,

2007; Resnick, 2001). Social capital can be very generally defined as obtaining resources from others (Coleman, 1988; Ellison et al., 2007). Studies have shown that SNS users are able to extend their social networks to numbers much larger than ever possible before modern SNSs, and thus potentially gain considerable social capital through their connections. Two types of social capital have been identified (Putnam, 2000). First, bridging capital refers to loose or weak ties that connect individuals who tend to be uninvested emotionally. Second is bonding capital, or capital gained from strong ties, such as those with family members and close friends.

Research in this area has investigated the power of SNSs to maintain or increase both types of social capital. For example, a recent study found that teens who use SNSs have higher social capital both offline and online (Ahn, 2012). This study also revealed that spending time on SNSs was linked to an increase in bridging capital and having positive or negative experiences on

SNSs was linked to bonding social capital.

A fourth area, one widely covered in mainstream news media, is the investigation of privacy issues on SNSs. Many investigations have focused on the personal information users are willing to divulge (Acquisti & Gross, 2006; Barnes, 2006; Debatin, Lovejoy, Horn, & Hughes,

2009; Gross, Acquisti, & Heinz, 2005; Stutzman, 2006; Tufekci, 2008). Most of this research suggests that young users of SNSs are generally not aware of just how public is the information they are sharing is. However, this research also suggests that users feel privacy is a major concern when using SNSs and yet act to the contrary—posting a relatively high amount of personal information on their profile pages without using appropriate privacy settings (Govani &

Pashley, 2005). In an attempt to offer a potential explanation for such behavior, Debatin and colleagues (2009) revealed a third-person phenomenon to exist in privacy perceptions: Users 33 perceived others as more susceptible to privacy invasion than themselves. The authors suggested that a change in this perception may be necessary to change users’ posting behaviors. Another and possibly related explanation is that those who have SNS accounts are less concerned overall with privacy. Tufekci (2008) found a significant relationship between SNS adoption and privacy concerns: Those more concerned with privacy were less likely become SNS users.

Others have investigated privacy concerns by the SNS platform or by demographic characteristics. Research has shown that users shared more information in Facebook than they would in Myspace because they typically viewed Facebook as more trustworthy (Dwyer, Hiltz,

& Passerini, 2007). Between the two genders, Lenhart and Madden (2007) found that girls were more concerned about disclosing their physical location than boys. In addition, older teens (15-

17) were more likely to post personal information, such as pictures of themselves, than younger teens (under 15).

Psychological effects of SNS usage. Although the four areas of research identified by boyd and Ellison (2007) collectively comprise a significant amount of SNS research, there is a growing yet underdeveloped area: the effects of spending time on SNSs on users. Indeed, some scholars have begun to focus on the positive and negative outcomes of spending time on SNSs

(Acar, 2008; Gonzales & Hancock, 2011; Haferkamp & Kramer, 2011; Stefanone, Lackaff, &

Rosen, 2011). So far, this research has covered multiple potential psychological outcomes.

Early in Internet research, Kraut and colleagues (1998) found that spending time online negatively impacted participants’ psychological well-being through increased levels of depression and loneliness. Since then, others have also found SNSs to negatively impact users’ psychological well-being (Barnes, 2003; Caplan, 2003; Nakayama, et al., 2009). For instance, another study found that those with higher levels of predispositional loneliness reported an even 34 more elevated level of loneliness by spending time on SNSs (Nakayama, et al., 2009). SNS use was also found to influence men’s value of self worth. In an experiment, men who viewed profiles of successful men perceived a larger gap between their actual and ideal professional accomplishments (Haferkamp & Kramer, 2011). Research has also shown that certain individuals are at a greater risk for potentially risky communication on SNSs. In one study, people with low self-esteem were more likely to talk to strangers on SNSs than those with high self-esteem (Acar, 2008).

Studies have also found positive effects of time spent on SNSs. For instance, research has demonstrated that spending time updating and viewing one’s profile on Facebook may actually increase self-esteem (Gonzales & Hancock, 2011). Exploring this phenomenon further, studies have shown a tendency for social compensation online: Those with lower levels of self-esteem offline increased their self-esteem by interacting with others on SNSs (Kraut et al., 2002; Ellison,

Steinfeld & Lampe, 2007; Walther, 1996). Still, others have found mixed results based on the type of feedback provided by other users within one’s friend network on SNSs. Positive feedback on profiles may enhance self-esteem and well-being, while negative feedback may decrease it (Nakayama et al., 2009; Valkenburg, Peter, & Schouten, 2006).

Enhancing Self-presentations on SNSs

Researchers have considered self-presentation and impression management on SNSs important because of the implications raised for one’s social identity (boyd, 2007; boyd &

Ellison, 2006; Donath, 2007; DiMicco & Millen, 2007; Ellison, Heino, & Gibbs, 2006;

Papacharissi, 2002; Tong, Van Der Heide, Langwell, & Walther, 2008; Zhao, Grasmuck, &

Martin, 2008). The findings can be summarized into a few propositions. First, studies have found that users strategically alter their self-presentations (e.g., pictures and descriptions of 35 physical and personal traits) on SNSs to present themselves better. For example, studies of online dating sites found daters to routinely embellish positive characteristics and conceal negative ones in an attempt to present an ideal self rather than the actual self (Brym & Lenton,

2001; Ellison et al., 2006; Hancock & Toma, 2009; Toma & Hancock, 2010; Yurchisin,

Watchravesringkan, & McCabe, 2005). The presentation of an ideal self on SNSs is part of the performance of impression management first described by Goffman (1959) (Papacharissi, 2002).

In one study, over one-third of online daters’ profile pictures were labeled inaccurate because of using old pictures, digital retouching, or professional pictures and demonstrated marked changes in hair styles and skin quality (Hancock & Toma, 2009). Interestingly, one study found that even though appearance-related manipulations were present, online daters perceived deception to be relatively low because of the anticipation for offline interaction (Ellison, Heino, & Gibbs, 2006).

Secondly, strategically altering one’s self-presentation is linked to cultural norms of attractiveness (boyd, 2007). For instance, Toma and Hancock (2010) found that less attractive online daters were more likely to enhance their profile pictures and lie about other physical descriptors like height and weight. Their analysis also demonstrated that daters’ deceptions were only related to physical appearance: Other profile characteristics such as income and occupation were not consistently embellished (Toma & Hancock, 2010). Arguably, online dating sites allow for more embellishment than other SNSs because they lack a list of contacts or friends, making them pseudonymous (Donath & boyd, 2004). Researchers have suggested that a list of contacts provides a certain level of accountability and trustworthiness of the information presented

(Donath, 2007; Donath & boyd, 2004). However, a Pew study on adolescents’ use of SNSs found that close to half admitted to presenting false information (Lenhart & Madden, 2007).

Indeed, when looking at photographs on SNSs, researchers have noted that digital photography 36 allows individuals to choose profile images that best represent most attractive self, as defined by cultural norms (Chalfen, 2002; Strano, 2001; 2008).

Third, edits to one’s self-presentation occurred not only in visual content, but in text as well. Most of the research on self-presentation discussed thus far has focused on visual cues.

However, text-based self-presentation can be equally important when exploring appearance norms on SNSs. For example, research on Friendster found that testimonials were commonly used to enhance one’s self-presentation (boyd & Heer, 2006). Also, one study found that SNS users were perceived as more attractive when their friends posted positive remarks on their wall

(Antheunis, & Schouten, 2011). In another example of impression management, Walther (2007) suggested that message composition is influenced by time afforded to craft messages on CMC and the editability afforded to create desired messages. The benefit of time allows SNS users to create self-presentations in line with the social norm of attractiveness through their messages.

Indeed, Walther found that users do take advantage of time affordances when crafting messages to put forth the most desirable self. For example, men took more time to edit their messages when communicating with women than with other men (Walther, 2007). Moreover, the attractiveness norm that users are striving to emulate may be embellished in the process of attempting to meet it. According to Walther (2007), “CMC may create dynamic feedback loops wherein the exaggerated expectancies are confirmed and reciprocated through mutual interaction”

(p. 2539). In an empirical test, the effects of perceived desirability of a communication partner influenced the language used by participants (Walther, 2007). Those who perceived their future conversation partner as desirable used significantly more personalized language and spent more time creating and editing their messages.

The fourth conclusion is that there are various motivations for enhancing one’s self- 37 presentation. Interest in building social capital within one’s group has been identified as a one motivation for altering one’s self-presentation (Zywica & Danowski, 2008). The authors proposed two hypotheses to explain this motivation, social enhancement and social compensation. Essentially, these contrasting hypotheses suggest that both the popular and unpopular offline are using SNSs to become popular online (i.e. to gain social capital). The authors found that those with social capital offline were also likely to develop this trait online, confirming the social enhancement hypothesis (also called “the rich get richer” hypotheses).

Conversely, participants also fulfilled the social compensation hypothesis (“the poor get richer”).

Those engaging in social compensation revealed more information about themselves online than offline, exaggerated information, and admitted to doing something to look popular on Facebook

(i.e. untagging pictures deemed undesirable, altering pictures to look more attractive, and posting funny pictures). Another study found similar compensation tendencies (Valkenburg et al., 2005).

The effect of these practices may be the creation of an online space with appearance norms that are not quite representative of the real world.

The fifth conclusion, one that has received much research attention, is that interaction with one’s peers can influence the self-presentation process and the adherence to social norms

(boyd, 2007; boyd and Ellison, 2008; Donath & boyd, 2004; McLaughlin & Vitak, 2012;

Papacharissi, 2002; Walther et al., 2008). boyd (2007) argued that the processes of impression management mirror that of socialization—that is, socialization occurs though learning how to manage impressions and adjusting to the social cues of others. The author further noted, “By looking at others’ profiles, teens get a sense of what types of presentations are socially appropriate; others’ profiles provide critical cues about what to present on their own profile” (p.

10). One study documented this process, revealing that personal homepage creators desired 38 community affiliation and altered their personal information to achieve this goal (Papacharissi,

2002). Two other studies found that having physically attractive friends’ post to one’s wall enhances one’s social attractiveness, or one’s popularity on SNSs (Antheunis & Schouten, 2011;

Walther et al., 2008). In other words, physical attractiveness of the company we keep can influence perceptions of our self-presentation online. From this self-presentation perspective, the moderating effects of peers’ presence on the amount of eating by women and men (Harrison et al., 2006) can be considered as an outcome of strategic self-presentation. In a body image study, participants with a discrepancy between their actual bodies and the bodies they ought to have demonstrated strategic eating behavior in their peers’ presence (Harrison et al., 2006). That is, the value placed on bulky men prompted male participants to eat more in front of others, and the emphasis on thinness prompted women to eat less.

As this fifth conclusion suggests, the strategic self-presentation is largely based upon one’s peer group. When in CMC environments, individuals present themselves based upon the cues received from those in their in-groups (boyd, 2007, McLaughlin & Vitak, 2012; Zhao et al.,

2008). For instance, research on normative behavior on Facebook found that posting pictures of oneself when he/she did not look their best was considered a norm violation (McLaughlin &

Vitak, 2012). So, group norms influence what is appropriate to post, and conversely, what is inappropriate as well. Research on social influence has shown that a salient group identity online can influence individual members to follow the group’s norms. The social identity model of deindividuation effects (SIDE) (Reicher, Spears, & Postmes, 1995) is one theoretical framework that has investigated the effects of a salient group identity on behavior in CMC settings.

Research from this perspective has demonstrated that a salient group identity may engulf one’s sense of self, leaving individuals to forgo their individuality to adopt a group identity (Matheson 39

& Zanna, 1990). The SIDE model may be a useful theoretical framework for the study of self- presentation online, and in particular, the effects of SNS profile pictures and comments on body image attitudes and behavior.

The SIDE Model Approach

The SIDE model provides a valuable theoretical framework for the field of media research to study group behavior in mediated contexts. In a series of studies investigating crowd behavior, Reicher and colleagues developed the social identity model of deindividuation (SIDE) from a social psychology perspective (e.g., Reicher, 1984; Reicher, Spears & Postmes, 1995). In creating the model, the authors drew from a social identity framework (Tajfel & Turner, 1979) that focuses on the collective self rather than an individualistic perspective (Hogg & Reid, 2006).

The major assumption of the SIDE model is that deindividuation occurs when a common group identity is more salient than one’s individual identity (Spear et al., 1990). Then, once deindividuated, individuals tend to closely follow the norms of the salient in-group (Terry &

Hogg, 2001). This has been labeled the cognitive component of the model (Klein, Spears, &

Reicher, 2007). After its initial conception and testing in face-to-face settings (Reicher, 1984), the model gained prominence in computer-mediated communication (CMC) research and has continued to be studied and expanded since (Lea & Spears, 1991; 1992; Postmes, Spears, and

Lee 1998; 2002; Lee, 2004; 2007a). In addition to the cognitive aspects of the SIDE model, researchers have started to focus on the strategic component which is closely linked to self- presentation research in CMC. The strategic aspect involves perceived expectations and/or possible sanctions individuals may incur based upon the norm of the salient group. Depending on individuals’ perceptions of what the group will accept, they may present themselves 40 differently from their personal identity when group identification is prominent (Barreto &

Ellemers, 2000).

Historical and Theoretical Origins of the SIDE Model

The origins of deindividuation theory can be traced back to early research on crowd behavior (LeBon, 1895; Allport, 1924), most notably LeBon’s idea of submergence. LeBon, in his work on crowd psychology, contended that the crowd is irrational and will become uniform based on social/emotional connections. LeBon also suggested that in its irrationality, the crowd becomes something uniquely different from the attributes of the individuals comprising it. The individual becomes submerged within the characteristics of the crowd. LeBon’s early work is relevant in that it influenced more recent conceptions of deindividuation theory (Zimbardo,

1969; Diener, 1977; Prentice-Dunn & Rogers, 1982) that are still present in research today.

Traditional deindividuation theory, as this research collective has been broadly labeled (Lea &

Spears, 1991; Reicher et al., 1995), suggests that individuals may lose themselves in groups or crowds, much as the way described by Lebon’s submergence.

However, in a review of crowd theory, Reicher (1987) addressed some of the shortcomings of the early crowd research such as political biases based on the “fear of the mob” mentality of the time (See LeBon, 1895; Allport, 1924). According to Reicher (1987), “LeBon’s crowd psychology may be read as a sustained attack upon collective protest” (p. 174). In addition, the importance of cognitive and social motivations is largely neglected from the traditional deindividuation perspective. Interestingly, these aspects of crowd behavior were addressed early on by Newcomb (1943). But it was arguably not until much later, after the traditional views of deindividuation that followed Lebon’s work were well established, that the social and cognitive aspects of social influence were thoroughly explored (Reicher, Spears, & 41

Postmes, 1995; Reicher, 1987). In a longitudinal study, Newcomb and colleagues (1943; 1967) found that college students with conservative attitudes left college—which the authors insisted to be an environment that embraced liberal ideology—with liberal norms and did not revert back to conservative ideology 25 years later. Addressing these findings from a social influence perspective, Turner (1991) observed that the most popular and active students, the leaders and individuals who identified with college in general, developed more liberal attitudes. It was the social identity approach, and self-categorization theory in particular, that provided the theoretical foundation for Turner’s arguments and established the basis for the creation of the SIDE model.

Self-categorization theory. Turner and colleagues (1987) developed the self- categorization theory from the social identity framework (Tajfel & Turner, 1979). As Turner

(1999) noted, “Self-categorization theory is, as a matter of record, a continuation of the tradition begun by social identity theory, extending and elaborating its idea that social identity processes are fundamental to understanding collective behavior” (p. 7). Self-categorization theory came in response to limitations of existing work on social influence in the form of the dual-process model.

First used by Deutsch and Gerard (1955; See Kaplan, 1987 for a recent summary), the dual process model suggests that there are two distinct types of influence, normative and informational. But, this division between the normative and the informational self was questioned by Turner et al. According to the dual process model, normative influence involves conforming to the expectations of the self and others. Informational influence is conformity to achieve what Festinger (1954) described as subjective validity, conformity to behavior that matches some correct or accurate information about reality. Informational influence has been described as largely cognitive in nature, or private attitude change (Turner, 1991). Scholars of social influence who ascribe to the dual process model have frequently labeled the normative 42 influence as the public self, and informational influence as the private or cognitive self (Turner,

1991).

Turner and colleagues (1987) questioned why this division has been made over and over again, arguing that the distinction between public and private self is problematic because it implies that social norms are not related to one’s private attitude(s). Turner (1991) posits that both public and private factors are involved in the formation and maintenance of social self, just as cognitive processes include normative and social elements. Therefore, the public and private selves are intrinsically linked, not separate units unrelated to each other.

Self-categorization theory suggests that individuals use a system of concepts (categories) to define self. Self-categorizations are “cognitive groupings of the self as identical, similar, equivalent to some class of stimuli in contrast to some other class” (Turner, 1991, p.155). Categorizations vary in the level of abstractness from individual differences or similarities to others (personal identity) to group member differences or similarities between out- groups and in-groups (social identity) (Turner et al., 1987). One’s in-group/out-group salience can magnify the perceived similarities to in-group members and perceived differences to the out- group. The more salient one’s social identity, the more depersonalized individual self-perception becomes. Shared characteristics become more important in defining one’s self than individual differences. An early attempt to empirically examine self-categorization theory showed that group salience increased self-stereotypes relevant to the group and produced behavior to match the group norms (Hogg & Turner, 1987).

Reicher (1987) provided a succinct definition of the self-categorization process. First, an individual ascribes him/herself to a specific social category (group). Second, the individual learns the norms of this category, which includes prototypicality (the most typical conceptions of 43 a norm). Finally, when the in-group category is salient, the individual will define themselves based upon the group norms and act accordingly. Self-categorization theory also included what has been labeled the meta-contrast ratio, or “the average perceived inter-category [norm] difference over the average perceived intra-category [norm] difference” (Turner, 1991, p. 156).

That is, when the difference between categories (in-group and out-group) is larger than the difference within (in-group), an individual is more likely to focus on an in-group identity over a personal identity. From this perspective, individuals define their social selves based on not only the similarities to their social group, but also the differences from other competing out-groups.

One study discussed how self-categorization theory is useful for research on social norms in communication disciplines (Hogg & Reid, 2006). A key concept from their chapter is social identity (group) salience. The authors suggested that social categorization can lead to normative behavior only if the social identity is psychologically salient. Additionally, group salience is enhanced through both accessibility, which refers to social categories that are frequently used (i.e. sex, race, etc.…), and fit, which refers to how good the categories are at describing the similarities and differences between people. Fit is closely tied to the meta-contrast ratio (Turner,

1991). Communication plays a key role in this process, yet its contribution in the self- categorization process remains relatively unexplored. Hogg and Reid went on to note the connection between the SIDE model and existing communication theories that investigate the perceptions of group norms, such as the third-person perception, pluralistic ignorance, framing, the spiral of silence, and media uses and gratifications. These theories are especially adept to the study of social norm formation from a media perspective (Hogg & Reid, 2006). In fact, two of these frameworks have already been examined from a social identity perspective: the third- person perception (Reid & Hogg, 2005) and pluralistic ignorance (Reid, et al., 2005). Further, 44 health promotion research has also found utility in the self-categorization perspective. As one study noted, health messages that promote a salient group identity to the target group and use a prototypical group member to do so have greater potential for success (Lipinski & Rimal, 2005).

The SIDE model as originally conceptualized. Reicher, Spears, and Postmes (1995) presented the SIDE model as an alternative to traditional deindividuation models (See section on deindividuation for more detail) that rest upon the idea that individuals may experience a loss of self when placed in group settings—when immersion or anonymity are being manipulated—that leads them to engage in anti-normative behavior. Conversely, from a social identity perspective, the SIDE model contends that the self is multi-dimensional, including a categorical (different social identities based upon context) and a personal self, and that “groups confer rather than destroy selfhood” (p. 177). The authors argued this point for CMC environments as well and direct empirical support for the SIDE model over other explanations has been well established

(e.g., Flanagin, Tiyaamornwong, O'Connor, & Seibold, 2002; Lea & Spears, 1991; Lee, 2004;

Postmes & Spears, 1998; Postmes, Spears, & Lea, 1998; 1999; Spears, Lea, & Lee, 1990).

The first empirical study to support the SIDE model was Reicher’s (1984) experiment investigating the effects of deindividuation with varying levels of group salience on behavior. In an experiment with a 2 X 2 between-subjects design, Reicher manipulated group immersion

(group vs. individual) and anonymity (anonymous vs. identifiable) using two distinct groups of students, those studying natural science and those studying social science. For the group condition, both groups were seated amongst themselves at two tables in the same room and told they would be tested as a group. They were referred to throughout the experiment by group number and used a group code to identify themselves on dependent measures. In the individual condition, members from the two groups were mixed together, and they were placed in rows 45 facing the front of the laboratory. They were told they were being tested as individuals and given individual codes. In the anonymous condition students were dressed in white baggy cloths and a cloth mask. To further distinguish the groups, the natural science students wore white masks and the social science students wore red. In the identifiable condition there was no change to the participants’ clothing. Participants watched a video on vivisection in which the natural scientists were projected as pro-vivisection, and the social scientists as anti-vivisection.

Results demonstrated strong support for the effects of group immersion. Natural science students were more pro-vivisection (in the group condition) and social scientists were more anti- vivisection. Deindividuation (via anonymity) also tended to increase pro-vivisection responses in the group condition for natural science students and decrease it in the individual condition, but had little effect on social science students. The authors later speculated that the group salience was so strong in the group condition that anonymity would have little further impact on group salience (Reicher et al., 1995). Overall, the findings suggest that deindividuation (as a function of anonymity) increased the salience of social identity, which led to an increase in normative behavior. Reicher demonstrated that group norms were embraced, not discarded, as the traditional deindividuation theory would suggest.

Main Constructs of the SIDE Model

1. Deindividuation. As previously noted, there are major differences between the traditional deindividuation theory and deindividuation as conceptualized in SIDE research. In fact, even within the traditional theory, there are two distinct approaches: classic and contemporary deindividuation theory. Festinger, Pepitone, and Newcomb (1952) first defined deindividuation as a phenomenon in which group members engage in anti-normative behavior when they are not given attention as individuals. Festinger et al.’s work prompted others to 46 investigate social influence from this position, such as Zimbardo (1969) and Diener (1977) in what has been labeled classic deindividuation theory. The classic theory suggests that deindividuation leads to anti-normative behavior due to reduced self-awareness. For example,

Diener and Wallbom (1976) demonstrated that cheating behavior among students increased with reduced self-awareness. In an important shift from face-to-face settings to the study of CMC environments, Diener (1980) argues that classic deindividuation theory helps to explain anti- normative behavior in CMC because of its anonymity. Anonymity is what Festinger (1954) referred to as a type of situational input, which is one of necessary variables for deindividuation to occur.

A variant to the classic perspective is that of contemporary deindividuation theory.

Contemporary deindividuation proposes a major change to the theory as described by Prentice-

Dunn and Rogers’s (1982) differential self-awareness theory: Self-awareness is not a unitary construct as in the classic theory, but instead consists of both public and private aspects, with the latter influencing deindividuation and anti-normative behavior. The authors (Prentice-Dunn &

Rogers’s, 1982) conducted an experiment to test the effects of reduced private self-awareness on behavior. The experiment used attentional cues (internal versus external cues) designed to enhance or take attention away from oneself, to manipulate private self-awareness. Specifically, in the external attentional cues condition participants were told to focus their attention outward, and that they were working on a group problem-solving project. In this condition, they also engaged in a task designed to increase group cohesiveness. The authors found that reduced private self-awareness leads to less thought to behavioral regulation. Although the contemporary theory has clear differences from the classic theory, both approaches still rest upon the basic hypothesis that deindividuation leads to anti-normative behavior. 47

The SIDE model approach counters the argument that group salience and deindividuation necessarily produce anti-normative behavior. Lea and Spears (1991) empirically tested the SIDE model in an attempt to explore deindividuation in CMC environments. The authors reconceptualize deindividuation from a social identity framework and suggested that deindividuation actually strengthens group norms. That is, immersion in a group increases the group’s prominence and members’ awareness of group norms. The authors also suggested that when group salience is high, visual anonymity reduces perceived intra-group differences, further strengthening the salience of the group norms. Postmes and Spears (1998) provided additional support for the SIDE model approach. In a meta-analysis of 60 deindividuation studies, the authors found that deindividuation, contrary to the traditional deindividuation theory, leads to an increase in normative behavior through manipulations of anonymity, group size, and reduced self-awareness, all of which are common means to achieve deindividuation. Indeed, support for the SIDE perspective over that of the traditional deindividuation theory has been consistently found in literature (Chan, 2010; Kugihara, 2001; Lea et al., 2001; Reicher & Levine, 1994;

Postmes, Spears, & Lea, 1998; Lee, 2006; 2007a; 2007b).

2. Polarization. The concept of polarization is another major point of divergence between the traditional and SIDE perspective of deindividuation theory. Kiesler, Siegel, and

McGuire (1984) provided the central argument for traditional deindividuation theory in CMC by suggesting that polarization is anti-normative behavior. Kiesler and colleagues argued that CMC is less personal, and thus allows more free communication. The absence of social norms to govern interaction, they maintained, tends to result in polarization. This perspective was widely accepted as an explanation for dysfunctional CMC behavior. 48

Alternatively, others (Turner et al., 1987; Turner & Oaks, 1986; 1989) advocated a self- categorization approach to group polarization research. Polarization from this perspective can be defined as the tendency for groups to become more extreme in the same direction to which they were already leaning (Moscovici & Zavalloni, 1969). The importance of communication in the polarization process has also been highlighted: “the mean response of members tends to become more extreme after group interaction in the same direction as the mean response before interaction” (Turner, 1991, p. 49). This approach was a move away from the more individualistic nature of the informational dependence model of conformity that suggested polarization was a result of interpersonal averaging (Allport, 1924; Asch, 1956; Festinger, 1954;

Sherif, 1936). One argument from the informational dependence model is that individuals strive for superiority over others (Isenberg, 1986). Before discussion, individuals do not know the position of others in the group, so they tend to stay moderate in their opinions, not moving too far from their perception of the group’s norm. Through discussion individuals will polarize toward the side of the norm to differentiate themselves from others. On the other hand, the self- categorization explanation corroborates with Paicheler’s (1977) view that groups tend to move toward the Zeitgeist—the person with the highest meta-contrast ratio (i.e., the most prototypical).

This claim helps to explain why the prototype is not the same as the pre-test mean of individual norms, and is in fact many times more extreme.

Two studies empirically contradicted the assertions put forth by Kiesler and colleagues and instead supported the SIDE model. Spears, Lea, and Lee (1990) improved upon Reicher’s

(1984) original experiment of the SIDE model in two ways. First, they independently manipulating group salience and deindividuation (as anonymity) whereas Reicher’s analysis confounded these two variables. Second, they employed the SIDE model in CMC for the first 49 time. Their findings—that there was greater polarization toward the norm when deindividuated and immersed in the group—demonstrated that polarization and conformity are not contradictory with each other. Lee (2007b) offered a more recent account along with several theoretical improvements to the SIDE model. Lee (2007b) expanded on Spears et al.’s (1990) work by specifically addressing the role of communication in the polarization process. Spears and colleagues manipulated norms prior to interaction by providing participants with a brochure that described results from a pretest survey that polled participants’ classmates about the norms. Lee did not include this manipulation, and, instead, observed the polarization process during the experimental group communication. In support of the SIDE model, Lee found that, as opposed to those in the individuated condition, those in the deindividuated condition fostered stronger identification with anonymous group members and were more likely to polarize their opinions in the direction of the norm. Thus, both studies support the SIDE model account for polarization.

3. Anonymity. The anonymity component of the SIDE model gave it its initial popularity in exploring early CMC environments. Many studies have supported the power of anonymity in CMC to strengthen group norms (Lea et al., 2001; Postmes & Spears, 1998; 2002;

Postmes, et al., 2001; Postmes, Spears, & Lea, 2002; Spears et al., 1990; Reicher, 1984; Walther et al., 2010). Several recent studies have not only explored anonymity from the SIDE perspective, but have also served to strengthen and expand the SIDE model in other ways. First,

Postmes et al. (2001) directly refuted traditional theory by showing that anonymity can strengthen social influence within a group, not weaken it. This was the first study attempting to measure the social influence process by incorporating manipulation of primed norms (Postmes et al., 2001). To manipulate primed norms, the authors used a sentence-scramble task that highlighted either prosocial traits or efficiency traits. To manipulate identifiability, participants 50 in the nonymous condition displayed a picture of themselves along with a user name.

Participants in the anonymous condition did not display any identifying information. After the sentence-scramble task was completed, participants then engaged in discussion with their group members regarding a proposed “dilemma”. The authors found that primed norms become norm through group interaction to a higher degree in anonymous situations. This finding also further enhances the applicability of the SIDE model to communication research by confirming that social influence is indeed a communicative process, not simply an intra-individual process.

In addition, Lea et al. (2001) expanded the SIDE model in two important ways by demonstrating that 1) visual anonymity leads to greater group attraction (contradicting classic deindividuation theory) and 2) both situational and categorical norms (gender, race) are important when forming perceptions. The authors cautioned future researchers to be deliberate and careful when operationalizing anonymity because anonymity can take many forms and how it is used has theoretical implications.

One study investigated the influence of YouTube comments on students’ perceptions of anti-marijuana PSAs (Walther et al., 2010). From the SIDE model perspective, the authors proposed that in the context of YouTube comments, visual anonymity, manipulated as a lack of identifiability, should enhance group salience while downplaying individual differences. Further, the increased group salience would lead to more normative behavior. Results supported the

SIDE model-based predictions: When communicating with anonymous peers, group identification increased social influence. The findings extended the model’s reach to a new

CMC context, YouTube.

Another recent study investigated the impact of visual anonymity in email on collective action (Chan, 2010). The author speculated that due to the laboratory setting common in SIDE 51 experiments, the theory may lack external and ecological validity. In support of the SIDE model,

Chan found that there was a significant interaction between communication channel (email vs. face-to-face) and group identification (low identification vs. high identification) among church group members on positive response to a call to action. That is, when group salience was increased, low identifiers with the church group donated more when they were solicited for donations via email than when solicited face-to-face. Based on these findings, the author suggested that email may be just as or more effective than face-to-face communication for soliciting donations.

In an attempt to refute the equalization hypothesis, the idea that individuals can interact more equally and freely in CMC because of the reduced impact of social norms (Dubrovsky,

Kiesler & Sethna, 1991), some SIDE model research has addressed the importance of categorical norms in anonymous CMC environments. Looking at the effects of categorical norms in anonymous CMC, Flanagin et al. (2002) investigated an interaction between gender and anonymity. The authors found that there were differences in how men and women acted strategically when engaging in anonymous CMC. Men attempted to make the CMC discussion more like face-to-face communication by disclosing more identifying personal information than women. On the other hand, women attempted to keep reduced social cues in CMC discussion.

Results demonstrated that gender differences in anonymous CMC serves to refute the technological determinist view of the equalization hypothesis (Dubrovsky et al., 1991) in support of the SIDE model.

Further investigating gender norms in CMC, Lee (2007a) examined the effects of character representation and knowledge bias (sports vs. fashion) on gender inference and informational social influence. Results from this analysis supported the SIDE model. Participants 52 attributed greater masculinity to partners who were identified by male character representations than those identified by female character representations. In the deindividuated context, there was “persistent influence of the social category membership and associated stereotypes” (p. 322).

Visual cues were important—even though arbitrary—in that participants conformed to the masculine character when asked sports questions. An important implication for future SIDE model research was that, in this study, gender-role stereotypes were not primed as they were is previous research (Postmes & Spears, 2002) and participants were not made to view themselves by their gender (Reicher, 1987, Spears et al., 1990). Yet, the effects were still significant. The results show the power of social categories to sway opinions in anonymous contexts. Further, the “men-know-sports-better” stereotype seemed to be salient and influential, prompting participants to conform. However, these effects were only found for stereotypes that favored men. Women did not enjoy the same privilege in the CMC environment to induce influence. Such a finding is further evidence against the equalization hypothesis.

Lastly, one study explored anonymity and the power of the in-group minority to exude social influence (Moral-Toranzo, Canto-Ortiz, & Gomez-Jacinto, 2007). The authors found that minority members of a group could change opinions in groups. That is, regardless of whether opinions come from a minority or majority member, anonymity can increase the group salience and opinions from within the group, and can influence the group in the direction of the opinions.

This study also demonstrated that communication plays an important role in the influence process since participants moved closer to the opinions of the source of influence in the post-test.

This finding confirms Postmes and colleagues’ (2001) previous results which showed that group members exhibited greater normative behavior after interaction. Moreover, there was no loss of private self-awareness in anonymous group, which calls contemporary deindividuation research 53

(Prentice-Dunn & Rogers, 1989) into question. Overall, the authors noted that the SIDE model is partially supported in two ways: 1) deindividuation led to more normative behavior; and 2) individuals strived to meet the prototype of group, which provide explanatory validity for the

SIDE model in CMC settings. However, contrary to the existing belief about the effects of anonymity in the SIDE model, there was no difference in the level of influence between anonymous and identifiable groups. For this, it was speculated that anonymity may not be crucial for deindividuation and group influence to occur, which was confirmed in a few other

SIDE studies (See Barreto & Ellemers, 2000; Lee, 2007a).

4. Group identification. Although anonymity is the variable that has most often been associated with the SIDE model, scholars have noted the importance of both anonymity and group identification in the deindividuation process (Postmes et al., 1999). Many studies have found a significant connection between group identification and the attitudes and behaviors of group members (Barreto & Ellemers, 2000; 2002; Ellemers et al., 2000; Lee, 2006; 2007; Jetten,

Spears, & Manstead, 1997; Kelly, 1993; Levine, Cassidy, & Jentzsch, 2010; Ouwerkerk, De

Gilder, & De Vries, 2000; Spears, Doosje, & Ellemers, 1997; Wann & Branscombe, 1990). As

Terry and Hogg (2001) noted, greater group identification leads to greater adherence to the norm.

In fact, Lee (2007a) suggested that group identification is the most important component of interaction for deindividuation to occur. On the other hand, research has shown that anonymity may affect behavior only when group identification is low (Barreto & Ellemers, 2002). Indeed, results from Lee’s (2006) experiment demonstrate that the link between deindividuation and increased conformity can be explained by the traditional SIDE assumption: Deindividuation leads to greater dependence on group norms, which results in increased conformity. This effect is typically mediated by group identification. That is, when group identification is manipulated to 54 be high in experimental settings, group members in depersonalized groups tend to conform to group norms. Therefore, group identification may be the most important factor leading to deindividuation and adherence to group norms.

Cognitive vs. Strategic Components of the SIDE Model

While most early studies of the SIDE model focused on the model’s cognitive component, scholars have slowly recognized the importance of its’ strategic component (Barreto & Ellemers,

2000; Ellemers et al., 2000; Hopkins et al., 2007; Klein, Spears, & Reicher, 2007; Tanis &

Postmes, 2003). According to Klein, Spears, and Reicher (2007), the cognitive component of the

SIDE model describes “the impact of deindividuation manipulations [such as immersion, anonymity, and reduced private focus] on the salience of self-categorizations” (p. 2). Essentially, the cognitive aspect explains the influence of group salience on group norm adherence, while the strategic component accounts for actual behavior employed to manage one’s salient group membership. The strategic element also highlights the connection between the SIDE model and self-presentation research. Indeed, Klein, Spears, and Reicher (2007) used the SIDE model to explore strategic identity performance and others have also suggested the SIDE model’s connection to self-presentation (Barreto & Ellemers, 2000; Reicher et al., 1995; Walther et al.,

2010). This idea was first noted by Reicher and colleagues (1995) in an earlier conception of the

SIDE model: “We suggest an integration of the two approaches whereby strategic considerations surrounding the presentation of self are combined with an analysis of which self is salient, the standards associated with that self and the implications for how subjects behave” (p. 192).

The strategic aspect of the SIDE model has been explored in CMC contexts with emphases on power and group status (Barreto & Ellemers, 2000; Ellemers et al., 2000; Flanagin et al., 2002; Spears et al., 2002). As previously noted, one study found that men and women use 55 different strategies when in anonymous CMC settings (Flanagin et al., 2002). That is, “men are more likely to seek ways to make CMC like face-to-face interaction, whereas women are more likely to employ strategies that maintain the reduced social cues of CMC and afford them more potential influence” (p. 82). This finding suggests that high and low status group members can and do use anonymous CMC strategically to meet their goals.

Investigating the effects of social cues on group behavior, Ellemers et al. (2000) found that those who perceived themselves to be in a low status group strategically displayed significantly more in-group favoritism when in private than when in public. That is, if individuals were aware that others knew they were in a low status group, they refrained from claiming to be in the group in public settings. The findings are consistent with self- categorization and the SIDE perspective: Salient norms add pressure that may prompt individuals to adapt their social behavior to the norm. Also, people refrain from behaving in line with their group membership when there is a fear of sanction by others (Reicher & Levine, 1994).

Spears et al. (2002) explored the potential for CMC as a channel for social resistance. In addition to the SIDE model, the authors utilized the power relation paradigm, which posits that the presence of other in-group members helps individuals resist powerful out-groups through perceived collective strength and socially supportive communication (Reicher et al., 1998). In the first of two studies, students were asked to discuss the issues of attendance and course work in small groups with two main manipulations: computer mediation (computer mediated versus no computer mediation) and visual anonymity (anonymous versus identifiable). The in-group norms on the two issues were in contrast to the norms of the powerful out-group (faculty). The second experiment manipulated perceived social support (support from peers versus no support from peers). The results support both the cognitive and strategic effects proposed in the SIDE 56 model: Students were more likely to adhere to norms in the anonymous condition than the visual cue condition (cognitive); and students were more likely to speak out in opposition to the out- group norm (faculty position on issue) even with the threat of sanction when in CMC and when they perceived support from other in-group members, regardless of anonymity. This has clear implication for research on social movements through strategic usage of social media. The findings suggest that when perceived group solidarity is high, the threat of sanction from a powerful out-group has less impact on communication in both mediated and face-to-face contexts.

A related study investigated the prosocial effects of group interaction, suggesting that group members may help others based on a strategy to improve one’s group identity (Hopkins et al., 2007). Results from their study showed that when a group stereotype is perceived as negative, in-group members will engage in behavior to combat the stereotype (in this example, being mean to others) within the group as well as when interacting with out groups.

Researchers have also stressed the difference between high and low identifiers in their adherence to the norm (Barreto & Ellemers, 2000). People who strongly identified with the group followed group norms regardless of the presence of visual cues. On the other hand, people who did not strongly identify with the group did not follow group norms when lacking visual cues (anonymous condition), but did so when held accountable (digital picture displayed after responses for other group members to see). The authors proposed that researchers should spend more time exploring the self-presentation (strategic) aspects of the SIDE model. Indeed, scholars have noted augmentation of self-presentation in the form of self-enhancement to meet social norms of attractiveness and popularity on Facebook (Zywica & Danowski, 2008). 57

However, explaining the mechanisms behind this process remains vague and disjointed

(Zywica & Danowski, 2008). As Barreto and Ellemers (2000) stated, “The study of both anonymous and accountable responses is likely to expand the relevance of work on social influence processes by rendering it applicable to both face-to-face groups (where people may be held accountable for their responses) and large-scale social categories (in which people tend to be more anonymous)” (p. 904). Such sentiments help to further support the need to focus on the strategic aspect of the SIDE model and its implication for self-presentation.

Tanis and Postmes (2003) also examined differences between high and low identifiers.

The authors examined the effects of restricted social cues on the ambiguity and positivity of impressions in three separate experiments. Results demonstrated that social cues (profile picture and/or short bio) have considerable influence on impression formation. That is, “Social cues give people a strong sense that they know with whom they are interacting, despite the fact that objectively their knowledge of the person is scant” (p. 690). The authors added that social cues tend to create a more positive impression. Additionally, the authors made a distinction between high and low identifiers—in the absence of social cues, high in-group identifiers chose in-group members as collaborators on a group task more often than did low identifiers.

The studies that have explored the strategic component of the SIDE model have consistently shown that when there is a salient group identity, individuals tend to adjust their behavior to the norms of the group. However, thus far, the SIDE model has not been used to explore this phenomenon on SNSs. As previously discussed, those that have investigated self- presentation on SNSs have found that users tend to alter their self-presentations to match the expectations of their salient online group. Perhaps the lack of attention from the SIDE model is due to the model’s tradition of exploring anonymous CMC environments. Indeed, robust 58 findings have been produced when deindividuation has been manipulated through anonymity.

Studies have demonstrated that in anonymous conditions, a lack of social cues heightens group salience, and individuals tend to follow group norms more often than when identifiable.

However, as previously discussed, recent studies using the SIDE model have demonstrated that group identification may be as, if not more, important than anonymity for deindividuation to occur.

Weaknesses of the SIDE Model

There are some inherent weaknesses of the SIDE model that are specifically tied to its use of experiments as its primary method of analysis. Critics of SIDE model research observe that the approach claims to explain too wide an array of CMC behavior (Walther, 2009; Walther et al., 2010). Specifically, Walther (2009) argues that SIDE model experiments typically test small groups that interact once for a short period of time and because of this, are unable to make claims to account for more encompassing CMC contexts. Indeed, most CMC is more continuous or ongoing than what a single-exposure experiment (typical of a SIDE model experiment) can capture. Also noteworthy in Walther’s critique is the focus of SIDE model experiments on small groups. It is true that many types of CMC are in fact not small group interactions, but are much larger in scale. A further critique of SIDE model experiments focuses on their reliance on simulated interaction instead of genuine/spontaneous communication between participants

(Walther et al., 2010; Walther, 2009; Wang, Walther & Hancock, 2009). That is, in a typical

SIDE experiment, participants communicate with contrived group members whose responses to questions presented during the manipulation have been pre-established by the researchers. This has been usually deemed necessary to establish control over the experimental setting, but is nevertheless a limitation. 59

The three criticisms levied against the SIDE model are all related to the issue of external validity. Still, critiques based on external validity could be made of most (if not all) true experiments and the ones presented here do not seem to be either fatal or extreme. In fact,

Walther (2009) further noted that by no means are these critiques grounds to abandon the model and that the “approach provides a very powerful account of some important aspects of online communication” (p. 750). What Walther and colleagues (2010) suggested is to enhance the

SIDE model’s method by expanding the context of investigation (e.g. forums, YouTube comment pages, etc.…), opening the communication to larger groups, and allowing for genuine interaction. The authors attempted all of these improvements in their study of comments made on YouTube in response to an anti-marijuana PSA. Only through innovative design and continued questioning of the model’s generalizability will the SIDE model be able to further reduce any threats to external validity.

Summary of SIDE Model Review

To briefly conclude, the SIDE model has evolved tremendously since its initial conceptualization, as has its incorporation into the communication field been. The SIDE model has almost been almost exclusively tested experimentally and, as noted above, in CMC environments. Traditional manipulations include anonymity/identifiability (Lee, 2007a; Moral-

Toranzo et al., 2007; Postmes, Spears, & Lea, 2002; Postmes et al., 2001), group salience (Lee,

2004; 2007a; Barreto & Ellemers, 2000; Spears, Lea & Lee, 1990; Tanis & Postmes, 2003) and deindividuation/depersonalization (Lee, 2006; Postmes et al., 2002; Spears, Lea & Lee, 1990).

More novel manipulations include public-self awareness (Lee, 2007a), primed norms (Postmes et al., 2001), local group norms, social category norms (Lea Spears, & de Groot, 2001), gender of group members (Flannigan et al., 2002), minority group member influence (Moral-Toranzo et al., 60

2007), status within the group (Ellemers et al., 2000), visual representation (Lee, 2004; 2007b;

Tanis & Postmes, 2003), and knowledge bias, and gender (Lee, 2007b). There has also been great variation in dependent measures. The following are some examples: group/social identification (Postmes et al., 2001; Lee, 2006); conformity (Lee, 2004; 2006; 2007b); private self-awareness (Postmes et al., 2001); local and social group categorization (Lea et al., 2001); group attraction, task focus, stereotypes of others (Lea et al., 2001); desire for information disclosure and gender representation (Flanagan et al., 2002); gender inference and perceived competence of partner (Lee, 2007a); positivity of impressions and willingness to interact with members of a different group (Tanis & Postmes, 2003); perception of individual and group task performance, strategy for status improvement, perception of in-group norm (Barreto & Ellemers,

2000); in-group favoritism (Ellemers et al., 2000).

Two relatively recent developments in SIDE model research are of particular relevance to the current study that explores body image issues on SNSs: 1) the expansion to include a strategic component as well as a cognitive component shows the model’s adaptability to explain a variety of group behavior and draws a connection between the SIDE model and the self- presentation perspective; and 2) Group identification is a more important factor than anonymity in affecting the deindividuation process, which supports the potential application of the SIDE model in nonymous CMC contexts like SNSs.

Lastly, the SIDE model’s focus on universal, categorical level norms has encouraged the use of specific universal level norms, such as the body ideal, in future experiments that test the theory. Indeed, scholars’ (Lea et al.’s, 2001; Lee, 2007b) discussions of both local and categorical norms raised such implications for researching the impact of potentially strong societal (categorical level) norms on behavior. For example, the body ideal has received 61 enormous attention over the past several decades and is arguably one of the most salient social norms in Western or Westernized societies today. Investigating its salience and relationship to deindividuation among SNS users may help to shed light on both the prominence of an unhealthy body ideal on SNSs and the usefulness of the SIDE model to investigate nonymous CMC settings.

Research Questions and Hypotheses

Based on the review of body image literature, self-presentation on SNSs, and the SIDE model, multiple research questions and hypotheses have been formulated to address the effects of specific features of SNSs (profile pictures and comments) on body image. In addition to the effects of SNSs on body image, this investigation will explore the moderating roles of both group identification and baseline body dissatisfaction. Each question and hypothesis, as well as their rationale, are presented henceforth. Appendix A displays a figure that demonstrates the relationship explored in each hypothesis.

Cognitive Effects of SNS Body Ideal Profile Pictures on Body Image

Although there is a solid foundation of research on the effects of traditional media consumption on body image (for a review see Barlett, Vowels, & Saucier, 2008; Grabe, Ward, &

Hyde, 2008; Levine & Harrison, 2009), there is a dearth of body image research in the context of

SNSs (Haferkamp & Kramer, 2011). Fortunately, burgeoning research on appearance-related self-presentations on SNSs (boyd, 2007; Ellison, Heino, & Gibbs, 2006; Papacharissi, 2002;

Zhao, Grasmuck, & Martin, 2008) recorded that SNS users strategically manipulate their profiles to become more attractive in the eyes of fellow users, which validates the importance of body image research in the context of SNSs. As Haferkamp and Kramer (2011) pointed out, the research to date has focused on SNS users as content producers, while research that concentrate 62 on the psychological consequences of using the content is severely lacking and thus in need of more attention.

Two existing studies that did examine this relationship found a significant connection between exposure to SNSs and body image (Haferkamp & Kramer 2011; Latzer et al., 2011). In one survey study, Latzer, Katz, and Spivak (2011) found that time spent on Facebook was highly correlated with body dissatisfaction, negative body image, and eating disorder pathology.

However, the authors did not identify which specific aspects of Facebook usage led to the negative effects. In the other study based on an experiment, attractive profile pictures were found to negatively impact body satisfaction (Haferkamp & Kramer, 2011). After exposure to attractive profile pictures, as opposed to either neutral or unattractive profile pictures, participants exhibited lower body satisfaction. However, the authors did not conceptually distinguish thinness and attractiveness, which leaves it unclear whether the observed effect was due to thinness or attractiveness of the models in the profile pictures. Thus, a study that isolates thinness from other related concepts would serve to clarify the mechanisms behind the process of deteriorating body satisfaction. Based on the discussed research, the following hypothesis is generated to examine the relationship between exposure to Facebook profile pictures and body image.

RQ1: What are the effects of exposure to body ideal profile pictures on Facebook on

users’ body image?

H1. Exposure to body ideal profile pictures will negatively impact users’ body image.

Cognitive Effects of SNS Body Ideal Comments on Body Image 63

A related and unexplored relationship is the effects of verbal messages in the form of

Facebook comments that support the body ideal on users’ body image. Examining this possible connection may help to further explain the effects of SNSs on body image. In a previous experiment, only the attractiveness of profile pictures was manipulated, keeping all other content consistent (Haferkamp & Kramer, 2011). Testing the effects of additional essential elements of

SNSs such as comments may be useful to demonstrate a more “realistic” SNS experience. Self- presentation research has shown that verbal cues are as important as visual cues to SNS users’ perceptions of attractiveness of the profile owner (Walther, 2007; Walther & Parks, 2002). The following hypothesis examines this relationship.

RQ2: What are the effects of exposure to body ideal comments on Facebook on users’

body image?

H2. Exposure to body ideal comments will negatively impact users’ body image.

In addition to the unique relationships between body ideal pictures and comments on body satisfaction, a third research question addressed the interaction between these two independent variables on body image.

RQ3: Is there an interaction effect between Facebook body ideal profile pictures and

body ideal comments on users’ body image?

H3: Users exposed to body ideal profile pictures and body ideal comments will exhibit

the lowest body image.

64

The Role of Predispositional Body Satisfaction

Multiple studies examining body image have found participants’ predispositional body satisfaction to influence the magnitude of the effects from exposure to idealized body images

(Arbour & Ginis, 2006; Hausenblas, et al., 2002; Henderson-King, et al., 2001; Posavac et al.,

1998; Stice et al., 2001; Stice & Shaw, 1994). For example, a meta-analysis of 25 female body image studies found that effects of exposure to media body ideal images were significantly stronger in those who had exhibited low body satisfaction in the pre-test. Moreover, the significant impact of baseline body dissatisfaction on post-test body image scores has also been found in studies of men’s body image (Arbour & Ginis, 2006; Hausenblas, et al., 2002). The experiment that found a significant relationship between SNS and body image (Haferkamp &

Kramer, 2011) did not measure baseline body dissatisfaction. However, based on the extensive support for the moderating effects of baseline body dissatisfaction on the relationship between media exposure and body image, the following two hypotheses were developed.

RQ4: Does participants’ predispositional body satisfaction affect the relationship

between exposure to body ideal profile pictures on Facebook and body image?

H4. Participants’ predispositional body satisfaction will moderate the relationship

between exposure to SNS body ideal profile pictures and body image in that those with

lower baseline body satisfaction will exhibit greater negative effects on their body image.

RQ5: Does participants’ predispositional body satisfaction affect the relationship

between exposure to body ideal comments on Facebook and body image? 65

H5. Participants’ predispositional body satisfaction will moderate the relationship

between exposure to body ideal comments and body image in that those with lower

baseline body satisfaction will exhibit greater negative effects on their body image.

The Moderating Effects of Group Identification

Although the visual and verbal cues may negatively impact Facebook users’ body image, research from the SIDE perspective has shown that group identification may be a crucial moderating variable. Research has shown that when individuals identify with a salient group in

CMC, they will engage in greater adherence to the norms of that group (Reicher et al., 1995;

Postmes & Spears, 1998) because individuals tend to pay more attention to their group identity than their personal identity when there is a salient group identity (Reicher et al., 1995; Postmes et al., 1998). This process labeled deindividuation leads individuals to conform to the norms of the salient group (Lea et al. 2001; Reicher, 1984; Reicher et al., 1995). In groups where striving to meet the body ideal is normative behavior, group members may feel less satisfied about their own bodies when their group identification is high. If the body ideal norm is made salient through idealized profile pictures and pro-ideal comments in a group where identification is high, individuals may feel greater pressure to adhere to those norms and thus experience greater negative effects on their body image. Based on this and the previous discussion of body image, two additional hypotheses have been formulated.

RQ6: Does group identification affect the relationship between exposure to body ideal

profile pictures on Facebook and body image? 66

H6. Group identification will moderate the relationship between exposure to body ideal

profile pictures and body image in that stronger group identification will result in lower

body image.

RQ7: Does group identification affect the relationship between exposure to body ideal

comments on Facebook and body image?

H7. Group identification will moderate the relationship between exposure to body ideal

comments and body image in that stronger group identification will result in lower body

image.

The Strategic Effects of SNS Body Ideal Comments on Adherence to the Norm

Research on the strategic component of the SIDE model has demonstrated that when group identification is high, individuals in CMC environments adhere to the norms of the group more than when identification is low (Barreto & Ellemers, 2000; Ellemers et al., 2000; Hopkins et al.,

2007; Klein, Spears, & Reicher, 2007; Tanis & Postmes, 2003). This has been especially evident in experiments that manipulated a threat of social sanction. That is, when individuals thought they would meet with their group members face-to-face after their CMC experience, or when their visual anonymity was removed, they were more likely to follow the norms of the group.

This investigation will employ a group identification factor by manipulating high and low levels of group identification, depending on the condition.

In addition, SIDE model research has found that specific overarching universal norms may dictate individual behavior in group settings. For instance, several studies found that gender roles significantly influenced behavior (Lea et al., 2001; Lee, 2007b). Since research has found 67 that SNS users routinely alter their self-presentation to appear closer to societal norms of beauty, the saliency of a societal level beauty norm among SNS users may be high. Also, the strategic aspects of the SIDE model has shown that individuals may adhere to the norms of a salient group even if they do not actually ascribe them privately. That is, the saliency of a group level beauty norm may also impact individuals’ behavior. To account for these potential influences, this study also includes a body ideal norm factor. So, in addition to manipulating the level of group identification, this study will also measure the impact of a salient body ideal norm on participants’ behavior. Based on the previous findings of the strategies individuals engage in to navigate their group membership and the potentially overarching norms of beauty on SNSs, the following hypotheses were created.

RQ8: Does exposure to body ideal profile pictures on Facebook affect participants’ body

ideal norm comments?

H8. Exposure to body ideal profile pictures will prompt participants to make pro-ideal

comments.

RQ9: Does exposure to body ideal comments on Facebook affect participants’ body

ideal norm comments?

H9. Exposure to body ideal comments will prompt participants to make pro-ideal

comments.

Again, a further research question and hypothesis will address the potential interaction between the two independent variables, profile pictures and comments, on participants’ 68 adherence to the norm.

RQ10: Is there an interaction effect between body ideal profile pictures on Facebook and

body ideal comments on participants’ body ideal norm comments?

H10: Participants exposed to body ideal profile pictures and body ideal comments will

make pro-ideal comments the most frequently.

Group Identification and Adherence to the Norm

As noted above, multiple studies using the SIDE model have found that group identification is an important moderating variable in online CMC group members’ adherence to the norm (Reicher et al., 1995; Postmes & Spears, 1998). Therefore, the following research question and hypothesis have been posed.

RQ11: Does group identification affect the relationship between body ideal comments on

Facebook and participants’ body ideal norm comments?

H11. Group identification will moderate the relationship between body ideal comments

and participants’ pro-ideal comments in that stronger group identification will result in

greater adherence to the body ideal norm.

Gender-based Differences

A final research question explores any gender differences in the effects on body image.

While the breadth of body image research that has focused on traditional media demonstrates that there are gender differences, the sole experiment on SNSs found no such differences 69

(Haferkamp & Kramer, 2011). Moreover, the current investigation will expose participants to ideal body images from both genders, while the aforementioned experiment used same-gender exposure in their stimuli. Therefore, four research questions are posed to explore potential gender differences.

RQ12: Are the effects of body ideal profile pictures on body image different between

men and women?

RQ13: Are the effects of Facebook body ideal comments on body image different

between men and women?

RQ14: Are the effects of Facebook body ideal profile pictures on participants’ pro-ideal

comments different between men and women?

RQ15: Are the effects of Facebook comments on participants’ pro-ideal comments

different between men and women?

70

CHAPTER 3: METHOD

Research Design

A pre-test post-test 2 x 2 X 2 between-group experimental design was used. The first factor was the body ideal profile pictures of four featured Facebook profile owners (2 men, 2 women) on the main status page that can be viewed upon logging into Facebook (body ideal vs. no-body ideal). The second was friends’ body ideal comments made on the profiles (pro-ideal vs. anti-ideal). The third factor was the participants’ level of group identification with the individuals featured in the profiles (high group identification vs. low group identification). See

Appendix A for the full research design.

Web-based Design

The web-based experiment was designed using the web design software, Dreamweaver

CS5, and included script writing in hypertext markup language (HTML), cascading style sheets

(CSS), and JavaScript (See Appendix B for stimulus code). In addition, query language

(MySQL) and hypertext preprocessor (PHP) scripts were used for the online database management of participants’ information on the server site, 1and1.com. For each participant, a

MySQL database retrieved and stored: responses from all pretest and post-test questions, participants’ comments made on the stimulus page, a unique identification number, a start time, a randomly assigned stimulus condition, and participants’ progress in the experiment (e.g. completed the pretest, began the stimulus, completed the stimulus, completed the post-test).

Initially, HTML, CSS and JavaScript formed the code foundation for the pretest and post- test questionnaires, as well as the stimulus material. All of the pretest and post-test questions, response options (e.g. text boxes, checkboxes, radio buttons, etc.) and tables were written using

HTML. A total of twelve HTML pages comprised the pretest and post-test combined. The study 71 was divided into two parts that were separated by approximately two weeks: the pretest and the stimulus/post-test. Participants were provided with a link to the first page of the pretest via email, and all those who completed the pretest were later sent a second email containing a link to the first page of the stimulus material/post-test. After clicking the links to access the first page of each section, each subsequent page was linked through a submit button. The submit buttons appeared at the bottom of each pretest, stimulus, and post-test page and not only brought participants to the next page, but also sent their responses to the online database located on the server provided by 1and1.com. A series of CSS linked the pretest and post-test pages so that all pages featured a similar background, color scheme, and structure. In addition, JavaScript validation was used to ensure that participants entered responses to all questions. The validation code prevented participants from moving on to the next page of the pretest before answering all questions on their current page.

MySQL tables and PHP scripts were used to obtain participants’ responses to pretest/post-test questions on the online database. First, the MySQL tables were created on the

1and1.com server so that every item in the table exactly matched the questions on the pretest/post-test. This allowed for the retrieval of participants’ responses through PHP scripts that existed in each HTML page of the experiment. Through PHP scripts, all participants, upon clicking the link to begin the pretest, were provided with a unique ID number (generated from the users’ IP address), start time, and randomly assigned stimulus condition. The ID was used to link participants’ responses from the pretest to their stimulus comments and their post-test responses. That is, PHP was used to: First, assign participants an ID number when starting the pretest; Second, create a mail script so that all those who completed the pretest were send an 72 email to begin the post-test; Third, retrieve and combine each participant’s responses from the pretest and post-test on the online database.

Similar coding techniques were also used to create the stimulus material. HTML and

JavaScript were used to create a stimulus page shell for all eight conditions. Screenshots of an actual Facebook status page were taken and divided into four sections (i.e. top, bottom, left, right) using Adobe Photoshop to create a border within the HTML shell page. For example, the top border appeared as the Facebook scroll bar (including a search textbox, and icons for friend requests, messages, and updates), and the left section appeared as the Facebook browsing menu

(including links to the news feed, friend lists, groups, and events). Because these four border sections were images taken of a Facebook status page, no links were functional. In addition,

Photoshop was used to blur these sections so that the contents were faded, but still somewhat visible. This was done so that participants’ focus would be primarily on the main stimulus material in the center of the HTML shell page. In the center of the shell page, a table was designed using HTML as well as PHP to create conditional statements. That is, the server-side

PHP code was incorporated so that content from one of the eight stimulus pages would appear in the center of the shell page based upon the stimulus condition to which each participant was randomly assigned at the start of the pretest. As previously noted, PHP was used to email all participants who completed the pretest with a link to begin the stimulus/post-test. Using PHP, each participant was emailed a unique link based upon their specific ID number and their previously assigned stimulus condition. When clicking on this link, participants viewed the mock Facebook status page that was comprised of the HTML shell and their assigned stimulus content. 73

Each stimulus condition was created on a separate HTML page. The pages were designed to resemble the actual status updates that appear in the center of a Facebook status page.

All stimulus pages featured updates from five Facebook friends. HTML was used so that the friends’ updates included profile pictures (that enlarged when scrolled over), text-based comments from the profile owners, responses from their Facebook friends, and a textbox for participants to leave their own comments. The comment textbox was accompanied by PHP code that retrieved participants’ comments and deposited them in a separate MySQL database table that was organized by each participant’s unique ID. This allowed for participants’ comments to be linked to the survey data.

After data collection was complete, the data stored in the online database were transferred to SPSS in a two-step process. First, the data were exported from MySQL into an excel spreadsheet. After an initial cleaning phase using excel, the data were then uploaded into SPSS for analysis.

Sample

Participants were undergraduate students at Bowling Green State University, OH, USA.

Since body image issues are evident in both men and women, both genders were recruited for the experiment. Participants were recruited from various courses throughout the campus and were offered extra credit in return. Because there were eight experiment conditions, approximately

1000 students were recruited to ensure an appropriate number of participants in each condition, after manipulation checks were conducted. The college student age group was deemed highly appropriate for the study based upon their frequent Facebook usage, which is among the highest of any age group (Facebook Statistics, 2011), and their higher propensity for body dissatisfaction than other young adults and older populations (Harrison, 2001). 74

Experimental Factors

Body Ideal Profile Picture Stimuli

In the body ideal profile pictures condition, participants viewed five profile pictures on a typical Facebook status page (see Appendix E for the Facebook page). Four pictures were of individuals who were judged to meet the ideal and the final picture was of a school logo

(Bowling Green State University for high group identification or California State Polytechnic

University, Pomona for low identification). In the no body profile picture condition, the same logo image and, instead of the four pictures each prominently featuring an ideal body type, images of scenery/landscape were used. To fully control the content displayed, all links were disabled.

To manipulate the body ideal profile picture condition, images from the site hotornot.com were downloaded. Initially, approximately ten images—five male and five female—rated as

“hot” were selected. Subsequently, ten independent raters determined the top four images—two male and two female—that fit the ideal by using a Likert scale from 1 (extremely unappealing body) to 10 (extremely appealing body). All independent raters were of the same age group as participants.

Body Ideal Comment Stimuli

On the status page, two of the four profiles owners (1 male, 1 female) had three comments from their friends in response to an initial post they made. On the actual Facebook site, those who leave comments in response to a post also have their profile picture featured.

Therefore, the current analysis featured a small profile picture next to each comment. No pictures were used that featured a user’s body. The images used were scenery, pets, and close-up face shots to minimize any unintended influence on the outcome measures. Participants were 75 told that the comments were actual statements from friends of the featured profile owners. The initial posts from the featured profile owners were uniform across the eight experimental conditions and focused on some aspect of the body ideal. For example, one post was “Ugh, I am feeling fat after the winter, any tips for shedding a few pounds?” Comments were either pro- ideal or anti-ideal depending on the condition. A pro-ideal comment read, “I feel the same.

Hitting up the gym hard since we got back from break!”, whereas an anti-ideal comment read,

“No way! I saw you the other day and you looked great!” To increase external validity, the comments mirrored or were rooted in actual remarks made by the Facebook friends in an actual user’s network.

Group Identification Stimuli

Participants in the high group identification condition received a script informing them that the profiles they were to view were of current students at their university. They were also instructed to proceed through the experiment while assuming that the profile owners are their close friends. In September of 2011, Facebook embedded a “close friends” group feature that allows users to group their close friends together and to view only their status feeds, if desired.

In addition, students in the high identification condition were informed that, once the experimental session was over, they were likely to be called upon to meet others who participated in the study in a face-to-face focus group session to discuss their answers. This manipulation was intended to result in a perceived “threat of social sanction,” which has been used in past SIDE research that investigated the strategic aspect of the model (Barreto &

Ellemers, 2000; Ellemers, Barreto, & Spears, 1999; Kim & Park, 2011; Spears, Postmes, Lea, &

Wolbert, 2002). In fact, Barreto and Ellemers (2000) noted that accountability is crucial for analysis of strategic processes, specifically responses to perceived sanctions: “The role of the [in- 76 group] audience is that of determining which norms must be attended to, as well as which self is to be presented” (p. 904).

Participants in the low group identification condition were informed that the profiles were from students at California State Polytechnic University, Pomona. Cal Poly, Pomona was chosen because of its’ stark contrast in geographic location from that of Bowling Green students’

Midwestern locale, as well as its’ potential to be lesser known among Bowling Green Students than larger California universities. Those assigned to this condition were also instructed to proceed through the experiment assuming the student profile owners were mere social acquaintances, or individuals that they do not know well. Lastly, they received no prompt for a perceived social sanction.

Manipulation Check

Body Ideal Profile Pictures. To confirm that participants’ took appropriate observation of the body ideal profile pictures, a manipulation check was conducted. The post-test asked participants to select two pictures that they viewed on the stimulus page out of four possible choices. Two were from the four pictures in the body ideal picture condition and two were from the four pictures in the no body picture condition. Only participants who chose the correct pictures were included in the final dataset.

Body Ideal Comments. Similarly, a manipulation check was conducted for the body ideal comment factor to confirm that participants’ took appropriate notice of the body ideal comments. The post-test asked participants to choose one response set of friends’ comments that they viewed out of two possible choices. One choice was identical to a response set from the pro- ideal condition and the other was identical to a response set from the anti-ideal condition. Like the profile picture factor manipulation check, participants were asked to identify the initial post 77 and response set that they viewed in the stimulus. Responses were coded as “pass” or “fail”, based on the condition participants were assigned.

Group Identification. To ensure the group identification factor had the desired effect, four post-test questions were asked from Luthanen and Crocker’s (1992) identity sub-scale. The questions have been amended for the current investigation. Responses to the questions were answered on a 7-point scale from “not at all” to “very much”: 1) “Overall, my affiliation to the students from the university displayed here have little to do with how I feel about myself.”

(reverse coded); 2) “Being a part of this university is an important reflection of who I am.”; 3)

“This university is unimportant to my sense of what kind of person I am.” (reverse coded); 4)

“In general, belonging to this university is an important part of my self-image.”

Dependent Measures

Body Image

Cash’s (2002) 34-item Multidimensional Body-Self Relations Questionnaire Appearance

Scales (MBSRQ) was employed to measure body image. The MBSRQ has consistently shown high validity and reliability when used to measure body image of men as well as women. The

MBSRQ is comprised of five categories: 8 items for appearance evaluation (AE), 11 items for appearance orientation (AO), 4 items for overweight preoccupation (OWP), 2 items for self- classified weight (SCW), and 9 items for body area satisfaction (BASS). The current analysis used these categories with confidence based upon the following high reliability alpha scores as reported by Cash (2000): appearance evaluation (W = .88, M = .88), appearance orientation (W

= .85, M = .88), overweight preoccupation (W = .76, M = .73), self-classified weight (W = .89,

M = .70), body area satisfaction (W = .73, M = .77). 78

Although each factor of the MBSRQ is related to perceptions of one’s body, they are all distinct measures (Cash, 2000). However, the appearance evaluation, overweight-preoccupation and body area satisfaction subscales are closely linked to one’s body satisfaction, whereas the appearance orientation and self-classified weight are more unique measures. First, appearance evaluation reflects individuals’ perceptions of their attractiveness level or satisfaction with their looks. For example, on a 5-point Likert scale from 1) definitely disagree to 5) definitely agree, participants are asked to comment on items such as: “My body is sexually appealing.” On the other hand, appearance orientation focuses on the importance given, the attention provided, and the grooming behaviors one engages in to “look good”. An example of appearance orientation is,

“Before going out, I usually spend a lot of time getting ready.” Third, overweight preoccupation concerns one’s anxiety toward being fat, their eating restraint, dieting behavior, and “weight vigilance” (Cash, 2000, p. 3). For example, participants are prompted to answer if they are on a weight loss diet. Fourth, self-classified weight assesses one’s perceptions of their current weight by asking two questions from 1) very underweight to 5) very overweight. The first inquires their perceptions of themselves, while the second asks participants to think about how other people perceive their weight. Finally, body area satisfaction reflects perceptions of satisfaction with areas of the body. The 9-item scale asks participants how they feel about specific aspects of their appearance, such as their upper torso (chest or breasts, shoulder, arms) or their hair (color, thickness, texture) from 1) strongly dissatisfied to 5) strongly satisfied.

Behavioral Adherence to the Norm

In all conditions, participants were asked to make at least one comment on the hypothetical Facebook status page. The comment was in response to a statement made by a profile owner who endorses the body ideal. The initial statement already had three responses 79 from other Facebook users for participants to view before they made their own comment. After the experiment, all comments were coded and interpreted as going with the body ideal norm, going against the body ideal norm, or neutral/irrelevant. This measure was taken to examine the strategic aspect of the SIDE model.

The following highlights the process of coding participants’ comments after data collection was completed. First, two-coders—one male graduate student and female undergraduate student—read through a number of comments and discussed their coding decisions. After the initial discussion, “mixed” and neutral/irrelevant categories also emerged. Comments were coded as mixed if they included both conforming and non-conforming themes. An example of a mixed comment from the dataset was, “exercising will definitely help, but I’m sure you look amazing anyways! :)” (See Appendix C for the codebook with examples of each coding category). After two training sessions, each coded 90 comments, approximately 10% of the sample, to establish intercoder reliability. Krippendorf’s reliability test revealed an alpha of .90.

At that point, the participant comments were split equally between the two coders to be analyzed independently.

Additional Variables of Interest

Media Use and the Media Attendance Model

Three types of media use were assessed. First, LaRose and Eastin’s (2004) media attendance model was adapted for use in the current analysis. In the statement that led with, “I use the Internet to…”, “Facebook” was exchanged for “Internet”. Five expected outcome subscales were used: activity outcomes (i.e., “…to cheer myself up.”); novelty outcomes (i.e.,

“…to get immediate knowledge of big news events.”); social outcomes (i.e., “…to get support from others.”); self-reactive “pass time” outcomes (i.e., “…to relieve boredom.”); and status 80 outcomes (i.e., “…to find others who respect my views.”). A 7-point Likert scale from 1) never to 7) always was used to record participants’ responses. In addition, a habit strength subscale (i.e.

“Facebook is part of my normal routine.”) and deficient self-regulation subscale (i.e. “I have a hard time keeping my Facebook use under control.”) were used. For these two subscales, a 7- point Likert scale was used from 1) strongly disagree to 7) strongly agree.

Second, television use was assessed by asking how many hours of television participants watched per day in the past week. Third, magazine use was assessed by asking participants how many issues they read per month of the following types of magazines: beauty, fitness, fashion, exercise, and health.

Procedure

The entire data collection took approximately four weeks. At recruitment, participants were asked to give their informed consent to participate in the experiment. Informed consent explains any potential risks and informs participants that they are volunteers and hence not required to participate for any reason (Singleton & Straits, 2005). During the informed consent process, the true purpose of the investigation was concealed so that this process did not influence the results. Participants were told that their participation would help to assess how people interact on social network sites.

Students first completed an online pre-test questionnaire (Appendix E) to establish baseline levels of body satisfaction. The questionnaire also collected their email, demographic information, media use information, and Facebook outcomes. Approximately two weeks later, participants were contacted again via email. The email also contained the URL to the experimental website. As previously noted, each participant was randomly assigned to one of the eight conditions upon clicking the initial link to start the experiment. 81

Before viewing the main stimulus material, the manipulation of group identification was introduced. After reading the group identification script, participants entered the main stimulus page that featured five profiles for view on a typical Facebook status page. The main status page also featured two posts, one each from two different profile owners (2 body ideal, 2 scenery).

Below the posts there were three comments from the profile owner’s friend network. All comments were pre-constructed and appeared when participants viewed the main stimulus material so that they were able to view them before making their own comment. Participants were then provided with the option to leave a comment in response to both of the two profile posts before they left the stimulus page. The comments, once made by participants, appeared at the bottom of the sequence of previously made comments, as they would when making a comment on Facebook. Following the stimulus, participants were asked to fill out a set of questions measuring the dependent variables and manipulation check items.

The final stage of the experiment process was a debriefing session. Kerlinger and Lee

(2000) stress the ethical obligations of this stage by emphasizing the need to reduce any possible embarrassment or foolishness participants may feel from being deceived. Given the potentially sensitive nature of this research, a thorough debriefing stage was required to ensure participants were not psychologically harmed in any way. Participants were informed of the true nature, purpose, and scope of the experiment. Contrary to what they had been told, those who received the group identification stimulus were informed that they would not be asked to participate in a follow up session that meets face-to-face. In addition, all participants were informed that the stimulus material was in fact fabricated and that no students from Bowling Green State

University or California State Poly Pomona participated or gave information for use in the study.

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Strategies for Data Analysis

All data were entered using SPSS. The following statistical analyses were used to answer the hypotheses and research question. Statistical significance for all analyses was set at the p

≤ .05 level.

H1 stated, “Exposure to body ideal profile pictures will negatively impact users’ body image.” Because this hypothesis compared the mean values of a continuous variable (DV: body image) across values of a categorical variable (IV: profile pictures), an ANOVA was used.

H2 suggested, “Exposure to body ideal comments will negatively impact users’ body image.” Because this hypothesis compared the mean values of a continuous variable (DV: body image) across values of a categorical variable (IV: comments), an ANOVA was used.

H3 suggested, “Users exposed to body ideal profile pictures and body ideal comments will exhibit the lowest body image.” Because this hypothesis asked about an interaction effect between two independent variables, body ideal profile pictures (categorical IV) and body ideal comments (categorical IV), on body image (continuous DV), a Factorial ANOVA was conducted.

The Factorial ANOVA included the one-way interaction as well as the two main effects.

H4 stated, “Participants’ predispositional body satisfaction will moderate the relationship between exposure to SNS body ideal profile pictures and body image in that those with lower baseline body satisfaction will exhibit greater negative effects on their body image.” Because this hypothesis investigated the relationship between two categorical independent variables (profile pictures and dichotomized predispositional body satisfaction) and a continuous dependent variable (body image), a Factorial ANOVA was used. When a Factorial ANOVA is used, the moderator variable is treated as a second independent variable (Mertler & Vannatta, 2010). The

Factorial ANOVA then considers all possible interactions and main effects for the factors. 83

H5 stated, “Participants’ predispositional body satisfaction will moderate the relationship between exposure to body ideal comments and body image in that those with lower baseline body satisfaction will exhibit greater negative effects on their body image.” Because this hypothesis investigated the relationship between two categorical independent variables (profile pictures and dichotomized predispositional body satisfaction) and a continuous dependent variable (body image), a Factorial ANOVA was used.

H6 stated, “Group identification will moderate the relationship between exposure to body ideal profile pictures and body image in that stronger group identification will result in lower body image.” Because this hypothesis examined the relationship between SNS body ideal profile pictures (categorical IV) on body image (continuous DV) when moderated by group identification (categorical moderator), a Factorial ANOVA was conducted.

H7 stated, “Group identification will moderate the relationship between exposure to body ideal comments and body image in that stronger group identification will lead to greater negative effects on body image.” Because this hypothesis was concerned with the effect of body ideal comments (categorical IV) on body image (continuous DV) when moderated by group identification (categorical moderator), a Factorial ANOVA was conducted.

H8 stated, “Exposure to body ideal profile pictures will prompt participants to make pro- ideal comments.” For this analysis, only participants’ comments that were pro-ideal or anti-ideal were included. All other comments made by participants, whether they were mixed, neutral/irrelevant, or missing, were treated as missing. Participants’ comments was thus treated as a dichotomous variable. Because this hypothesis compared a dichotomous independent variable (body ideal profile pictures) and a dichotomous dependent variable (participants’ 84 comments), a Chi-square analysis was used to find out which type of participant’s comments

(pro-ideal/anti-ideal) were more likely to be generated by body ideal profile pictures.

H9 stated, “Exposure to body ideal comments will prompt participants to make pro-ideal comments.” Because this hypothesis compared a dichotomous independent variable (body ideal comments) and a dichotomous dependent variable (participants’ comments), a Chi-square analysis was used to find out which type of participant’s comments (pro-ideal/anti-ideal) were more likely to be generated by body ideal comments.

H10 stated, “Participants exposed to body ideal profile pictures and body ideal comments will make pro-ideal comments the most frequently.” Because this hypothesis examined the influence of two dichotomous independent variables (body ideal profile pictures and body ideal comments) on a dichotomous dependent variable (participants’ comments), a Chi-square analysis was used to find out whether the interaction between pictures and comments affected participant’s comments. Only the cases where participants made pro-ideal comments were selected for the analysis and, subsequently, a Chi-square analysis between body ideal profile pictures and body ideal comments was conducted to see which cell had the highest number of pro-ideal comments, and if the differences among cells were statistically significant.

H11 stated, “Group identification will moderate the relationship between body ideal comments and participants’ pro-ideal comments in that stronger group identification will result in greater adherence to the body ideal norm.” Because this hypothesis examined the influence of two dichotomous independent variables (body ideal comments and group identification) on a dichotomous dependent variable (participants’ comments), a Chi-square analysis was used to find out whether the interaction between body ideal comments and group identification affected participant’s comments. Only the cases where participants made pro-ideal comments were 85 selected for the analysis. Then, a Chi-square analysis between body ideal comments and group identification was conducted to see which cell had the highest number of pro-ideal comments, and if the differences among cells were statistically significant.

RQ12 inquired, “Are the effects of body ideal profile pictures on body image different between men and women?” Because this question asked whether the relationship between body ideal profile pictures (categorical IV) and body image (continuous DV) was different between men and women, a Factorial ANOVA was conducted. The Factorial ANOVA considered all possible interactions and main effects for the factors.

RQ13 inquired, “Are the effects of Facebook body ideal comments on body image different between men and women?” Because this question asked whether the relationship between body ideal comments (categorical IV) and body image (continuous DV) was different between men and women, a Factorial ANOVA was conducted. The Factorial ANOVA considered all possible interactions and main effects for the factors.

RQ 14 inquired, “Are the effects of Facebook body ideal profile pictures on participants’ pro-ideal comments different between men and women?” Because this hypothesis examined the influence of two dichotomous independent variables (body ideal profile pictures and gender) on a dichotomous dependent variable (participants’ comments), a Chi-square analysis was used to find out whether the interaction between body ideal profile pictures and gender affected participant’s comments. Only the cases where participants made pro-ideal comments were selected for the analysis. Then a Chi-square analysis between body ideal profile pictures and gender was conducted to see which cell had the highest number of pro-ideal comments, and if the differences among cells were statistically significant. 86

RQ 15 asked, “Are the effects of Facebook comments on participants’ pro-ideal comments different between men and women?” Because this hypothesis examined the influence of two dichotomous independent variables (body ideal comments and gender) on a dichotomous dependent variable (participants’ comments), a Chi-square analysis was used to find out whether the interaction between body ideal comments and gender affected participant’s comments. Only the cases where participants made pro-ideal comments were selected for the analysis. Then a

Chi-square analysis between body ideal comments and gender was conducted to see which cell had the highest number of pro-ideal comments, and if the differences among cells were statistically significant.

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CHAPTER 4: RESULTS

Preliminary Analyses

After the data cleaning and screening processes, preliminary analyses were conducted to view basic frequency output for the variables of interest, to recode and create any new necessary variables from the existing data, and to check for successful manipulation of the three experimental factors.

Participant Demographic Information

The pretest was administered online during the first week of April, 2012 at Bowling

Green State University. Students of consenting instructors were contacted via email with a link to begin their participation in the study. Of roughly 2500 students contacted, 1023 students consented and accessed the pretest, and a total of 950 completed it successfully. At the start of the pretest, all participants were automatically randomly assigned to one of the eight stimulus conditions. Approximately two weeks after administration of the pretest, students who successfully completed the pretest were emailed a link to the post-test website. Upon clicking the link, participants were directed to their pre-assigned stimulus conditions. 860 participants successfully completed both the pre-test and post test. After performing the three manipulation checks, the number was reduced to 501. 167 (33.3%) participants were men, 330 (65.9%) were women, and 4 (0.8) categorized themselves as other (see Table 1). The mean age was 19.98 (SD

= 2.22). Most participants were Caucasian (N = 405, 80.8%), with African Americans as a remote second (N = 58, 11.6%). In very small numbers, some participants were Hispanic (N = 8,

1.6%), Asian (N = 8, 1.6%), and Native American (N = 2, 0.4%). 20 participants (4.0%) reported their ethnicity as Other. 88

Manipulation checks. Three manipulation checks were used (see Table 2). First, the overall mean for group identification was 4.00 (SD = .86). Next, participants registering a mean above 5 in the low group identification condition and below 3 in the high group identification condition were considered to have failed the manipulation and thus dropped from the analysis.

This reduced the sample size from 866 to 770. Then, a one-way ANOVA was conducted to determine the difference in mean group identification scores between the low and high group identification conditions. Results demonstrated that the mean group identification score for participants in the low identification group (M = 3.78, SD = .89) was significantly lower than the high identification group (M = 4.26, SD = .69), F (1,768) = 68.698, p < .001.

Second, a manipulation check was conducted for the body ideal profile picture factor to ensure participants paid sufficient attention to the pictures. On the post-test, participants were shown four pictures, two of which were chosen from the four pictures in the body ideal picture condition and two from the four pictures in the no body picture condition. Participants were asked to identify the two pictures that they viewed on the stimulus page. Responses were coded as “pass” or “fail”, based on the condition participants were assigned. Of the 770 participants,

505 (65.6%) successfully recognized both pictures they saw on the stimulus website.

Third, a manipulation check was conducted for the body ideal comment factor to ensure that an adequate amount of attention had been paid to the comments. On the post-test, participants were shown two initial posts and their corresponding friends’ responses, one identical from the pro-ideal condition and one from the anti-ideal condition. Like the profile picture factor manipulation check, participants were asked to identify the initial post and response set that they viewed in the stimulus. Responses were coded as “pass” or “fail”, based on the condition participants were assigned. Of the 770 participants, 727 (94.4%) successfully 89 passed the comment manipulation check. When the body ideal picture and body ideal comment manipulation checks were combined, 501 (65.1%) participants passed both. Based on results from the three manipulation checks, a final dataset containing responses from 501 participants was used for the remaining analyses.

Body Ideal Norm-Conforming Comments

Overall, 320 (63.9%) participants produced pro-ideal comments and 82 participants

(16.4%) produced anti-ideal comments. Comments of 99 participants (19.6%) were coded as missing (see Table 3). The breakdown of the missing cases was as follows: 9 left no comment

(1.8%), 54 (10.8%) were mixed, and 36 were neutral or irrelevant (7.0%).

Participant Media Usage

When asked if they had logged in to Facebook in the past 24 hours, 90.6% of participants said yes, while only 9.4% said no (see Table 4). The average number of years participants had been using Facebook was 4.32 (SD = 1.41), and the mean daily hours spent on Facebook in a typical week was 2 hours and 49 minutes (SD = 4.96). Participants reported spending slightly more daily hours watching television, 3 hours and 5 minutes (SD = 4.04). Participants were also asked how many issues they read per month of specific types of magazines. The most read were style/fashion magazines (0.66 issues, SD = 1.19), followed by entertainment magazines (0.58 issues, SD = 1.16) and fitness/health magazines (0.30 issues, SD = 0.75). No participants read sports magazines on a regular basis.

Number of Facebook Friends. Participants most commonly reported having between

301-450 (19%) friends, followed closely by 451-600 (18.6%) friends, then 601-850 (18%) friends. A total of 20.8% of participants had less than 300 friends (0-150 friends, 8.2%; 151-300 friends, 12.6%). 23.8% of participants reported friend counts over 850 (851-1000 friends, 7.0%; 90

1001-1250 friends, 8.6%; over 1250 friends, 8.2%). The overall mean was 4.22 (SD = 1.98), which places the average number of friends reported between the ranges of 450-600 and 601-850.

Facebook Activities. On a 7-point Likert scale from 1) never to 7) always, participants were also asked about their Facebook updating and posting behavior. Comment posting was the most common Facebook activity (M = 4.65, SD = 1.53). After comments, participants most often posted personal updates (M = 3.96, SD = 0.97), followed by pictures of themselves (M =

3.89, SD = 1.37), and pictures of their friends (M = 3.63, SD = 1.57). Participants rarely posted videos (M = 3.03, SD = 1.57) or pictures (M = 3.00, SD = 1.66) from other sites, and very rarely posted news stories (M = 2.74, SD = 1.55), videos of their friends (M = 1.78, SD = 1.06), or videos of themselves (M = 1.66, SD = 0.97).

Motivations for Facebook Use. Results demonstrated that none of the five expected outcomes subscales had a mean score above the mid-point, 4, “somewhat agree” (see Table 5).

Of the five subscales, self-reactive outcomes had the highest mean at (M = 3.88, SD = 1.34), followed by social, (M = 3.14, SD = 1.36), novel, (M = 2.87, SD = 1.26), activity, (M = 2.85, SD

= 1.05), and status outcomes (M = 2.74, SD = 1.36). For the remaining two subscales, the habit strength mean was 4.49 (SD = 1.57) and the deficient self-regulation mean was 2.11 (SD = 1.13).

Conditions/Stimulus Cells. The three experimental factors were body ideal profile pictures (pro-ideal pictures vs. no body pictures), body ideal comments (pro-ideal comments vs. anti-ideal comments) and group identification (high identification vs. low identification). Of the

501 participants included in the final dataset, 259 (51.7%) participants were in the body ideal picture condition and 242 (48.3%) were in the no body picture condition (see Table 6). Also,

251 (50.1%) participants were exposed to pro-ideal comments and 250 (49.1%) were exposed to 91 anti-ideal comments. Lastly, 243 (48.5%) participants were in the high group identification group and 258 (51.5%) in the low group identification group.

The final number of participants in each experimental condition was fairly even across all eight cells (see Table 6): 1) body-ideal pictures, pro-ideal comments, high group identification,

58 (11.6%); 2) body-ideal pictures, anti-ideal comments, high group identification, 67 (13.4%);

3) no body pictures, pro-ideal comments, high group identification, 55 (11%); 4) no body pictures, anti-ideal comments, high group identification, 63 (12.3%); 5) body-ideal pictures, pro- ideal comments, low group identification, 75 (15%); 6) body-ideal pictures, anti-ideal comments, low group identification, 59 (11.8%); 7) no body pictures, pro-ideal comments, low group identification, 63 (12.6%); and 8) no body pictures, anti-ideal comments, low group identification, 61 (12.2%).

Body Image. The 34 items of Cash’s (2000) MBSRQ were split into five previously established categories: 8 items for appearance evaluation (AE), 11 items for appearance orientation (AO), 4 items for overweight preoccupation (OWP), 2 items for self-classified weight

(SCW), and 9 items for body area satisfaction (BASS). High alpha reliability scores were obtained for all five MBSRQ subscales: AE (0.88), AO (0.85), OWP (0.80), SCW (0.86), BASS

(0.86), which supports previous research using this scale (Cash, 2000). Participants’ mean scores were: AE (M = 3.40, SD = .78), AO (M = 3.36, SD = .64), OWP (M = 2.57, SD = .97), SCW (M

= 3.21, SD = .73), and BASS (M = 3.45, SD = .73) (see Table 7).

Predispositional Body Satisfaction. Predispositional body satisfaction was measured using the 9-item BASS sub-scale during the pretest. A mean score of 3.61 (SD = .76) was recorded. A mean split was used to separate participants into low (all scores below the mean) and high (all scores above the mean) predispositional body satisfaction groups. 240 (47.9%) 92 participants were classified into the low predispositional body satisfaction group and 261

(52.1%) were into the high predispositional body satisfaction group (see Table 4).

When comparing gender, the overall mean for men was 3.75 (SD = .71), higher than the mean score for women, 3.53 (SD = .78). This difference was statistically significant, F (1, 495)

= 9.144, p < .01. In addition, out of 330 women, 172 (52.1%) were below the mean split of 3.61 and therefore coded as having low predispositional body satisfaction; 158 (47.9%) were above the mean and thus were recorded as having high predispositional body satisfaction. For men, 67

(40.1%) out of 167 were below the mean, and 100 (59.9%) were above the mean.

Research Question and Hypothesis Testing

RQ1: What are the effects of exposure to body ideal profile pictures on Facebook on users’ body image? H1 predicted that exposure to body ideal profile pictures on Facebook would negatively impact users’ body image. One-way ANOVA was conducted with profile pictures as the independent variable and body image as the dependent variable. Five sub- categories within the MBSRQ were tested separately, including appearance evaluation (AE), appearance orientation (AO), overweight preoccupation (OWP), self-classified weight (SCW), and body area satisfaction (BASS). No significant differences emerged between participants exposed to body ideal pictures and those exposed to no body pictures for any of the MBSRQ measures (see Table 8). More specifically, there were no significant differences between those exposed to body ideal pictures (M = 3.40, SD = 0.83) and those exposed to no body pictures (M

= 3.39, SD = 0.72) in AE, F (1, 499) = .015, p > .05. Also, the two conditions were nearly equivalent for AO. The mean AO of the body ideal picture group was 3.35 (SD = 0.68) and the mean of no body picture group was 3.36 (SD = 0.59), F (1, 499) = .056, p > .05. In addition, participants’ OWP scores did not differ significantly between those exposed to body ideal 93 pictures (M = 2.56, SD = 1.00) and those exposed to no body pictures (M = 2.59, SD = 0.94), F

(1, 499) = .119, p > .05. There were also no significant differences in mean scores for the fourth category, SCW, between those exposed to body ideal pictures (M = 3.22, SD = 0.75) and those exposed to no body pictures (M = 3.19, SD = 0.71), F (1, 499) = .113, p > .05. Finally, BASS scores did not vary significantly between those exposed to body ideal pictures (M = 3.48, SD =

0.78) and those exposed to no body pictures (M = 3.43, SD = 0.66), F (1, 499) = .503, p > .05.

Hypothesis 1 was not supported.

RQ2: What are the effects of exposure to body ideal comments on Facebook on users’ body image? H2 predicted that exposure to pro-ideal comments on Facebook would negatively impact users’ body image. One-way ANOVA was conducted with friends’ comments as the independent variable and body image as the dependent variable. Again, the five sub- categories within the MBSRQ were tested: appearance evaluation (AE), appearance orientation

(AO), overweight preoccupation (OWP), self-classified weight (SCW), and body area satisfaction (BASS). As with body ideal pictures, no significant differences emerged between participants exposed to pro-ideal comments and those exposed to anti-ideal comments for any of the MBSRQ measures (see Table 9). For AO scores, there were no significant differences between those exposed to pro-ideal comments (M = 3.37, SD = 0.64) and those exposed to anti- ideal comments (M = 3.42, SD = 0.75), F (1, 499) = .481, p > .05. Likewise, for AE scores, there were no significant differences between those exposed to pro-ideal comments (M = 3.33, SD =

0.64) and those exposed to anti-ideal comments (M = 3.39, SD = 0.64), F (1, 499) = .637, p > .05.

In addition, there was no significant difference in OWP scores between those exposed to pro- ideal comments (M = 2.61, SD = 0.99) and those exposed to anti-ideal comments (M = 2.53, SD

= 0.95), F (1, 499) = .813, p > .05. Similarly, there was no significant difference in SCW scores 94 of those exposed to pro-ideal comments (M = 3.26, SD = 0.71) and those exposed to anti-ideal comments (M = 3.16, SD = 0.74), F (1, 499) = 2.305, p > .05. Finally, BASS scores did not vary significantly between those exposed to pro-ideal comments (M = 3.42, SD = 0.75) and those exposed to anti-ideal comments (M = 3.48, SD = 0.70), F (1, 499) = .856, p > .05. Hypothesis 2 was not supported.

RQ3: Is there an interaction effect between Facebook body ideal profile pictures and body ideal comments on users’ body image? H3 predicted that users exposed to body ideal profile pictures and body ideal comments would exhibit the most negative body image.

Five factorial ANOVAs were conducted with the picture and comment factors as independent variables and a body image subscale as the dependent variable (see Table 10). The interaction between profile pictures and comments was significant for none of the five MBSRQ subscales:

AE, F (1, 497) = .019, p > .05; AO, F (1, 497) = 3.371, p > .05; OWP, F (1, 497) = 1.364, p

> .05; SCW, F (1, 497) = .001, p > .05; BASS, F (1, 497) = .062, p > .05. Therefore, Hypothesis

3 was not supported.

RQ4: Does participants’ predispositional body satisfaction affect the relationship between exposure to body ideal profile pictures on Facebook and body image? H4 suggested that participants’ predispositional body satisfaction would moderate the relationship between exposure to body ideal profile pictures and body image in that those with lower baseline body satisfaction would exhibit greater negative effects on the MBSRQ subscales. Five factorial

ANOVAs were conducted with the picture factor as the independent variable, a body image subscales as the dependent variable, and dichotomized predispositional body satisfaction as the antecedent moderator variable (see Table 11). 95

According to the analysis results, the interaction effects between body ideal profile pictures and predispositional body satisfaction were significant for three MBSRQ subscales: AE,

F (1, 497) = 6.430, p < .05; OWP, F (1, 497) = 4.389, p < .05; and BASS, F (1, 497) = 4.389, p

< .001. Participants with low predispositional body satisfaction exhibited lower scores on the

AE and BASS subscales after exposure to body ideal profile pictures (AE: M = 2.86, SD = 0.72;

BASS: M = 2.92, SD = 0.58), as opposed to no body pictures (AE: M = 2.97, SD = 0.69; BASS:

M = 3.02, SD = 0.55). On the contrary, those with high predispositional body satisfaction displayed higher scores on the same subscales in the body ideal picture condition (AE: M = 3.92,

SD = 0.55; BASS: M = 4.01, SD = 0.53) than in the no picture condition (AE: M = 3.76, SD =

0.52; BASS: M = 3.79, SD = 0.52). In addition, participants with low predispositional body satisfaction reported higher scores on the OWP subscale after exposure to body ideal pictures (M

= 3.01, SD = 0.98) than to no body pictures (M = 2.88, SD = 0.94); those with high predispositional body satisfaction exhibited lower OWP in the body ideal picture condition (M =

2.13, SD = 0.81) than in the no body picture condition (M = 2.33, SD = 0.87). Because higher scores on the OWP subscale represent a higher level of fat anxiety, the higher OWP mean of participants with low predispositional body satisfaction indicated lower satisfaction with the fat level of their bodies. There was no interaction effect either for AO, F (1, 497) = .135, p > .05 or

SCW, F (1, 497) = .827, p > .05. Because there were three significant interaction effects out of five in the anticipated direction, H4 received partial support.

RQ5: Does participants’ predispositional body satisfaction affect the relationship between exposure to body ideal comments on Facebook and body image? H5 predicted that participants’ predispositional body satisfaction would moderate the relationship between exposure to body ideal comments and body image. More specifically, those with lower baseline 96 body satisfaction were expected to exhibit greater negative effects on their five subscales of body image. Five factorial ANOVAs were conducted, each with friends’ comments as the independent variable, a body image subscale as the dependent variable, and predispositional body satisfaction as the moderator variable (see Table 12).

The interaction between pro-ideal comments and predispositional body satisfaction were not significant for AE, F (1, 497) = 1.166, p > .05; AO, F (1, 497) = .389, p > .05; OWP, F (1,

497) = .079, p > .05; SCW, F (1, 497) = 1.441, p > .05; or BASS, F (1, 497) = 1.037, p > .05.

Because there was no significant interaction effect for any of the five body image subscales, predispositional body satisfaction was determined not to be a significant moderator of the relationship between the comment factor and body image. Hypothesis 5 was not supported.

RQ6: Does group identification affect the relationship between exposure to body ideal profile pictures on Facebook and body image? H6 predicted that group identification would moderate the relationship between exposure to body ideal profile pictures on Facebook and body image in that stronger group identification would result in more negative body image.

Five factorial ANOVAs were conducted with body ideal pictures as the independent variable, a body image subscale as the dependent variable, and group identification as the moderator variable (see Table 13). The moderator variable was treated as a second independent variable.

The analysis considered all possible interactions and main effects for the factors.

First, as reported in H1 testing results, main effects results revealed no significant difference between the two picture conditions (AE, F (1, 497) = .016, p > .05; AO, F (1, 497)

= .070, p > .05; OWP, F (1, 497) = .122, p > .05; SCW, F (1, 497) = .121, p > .05; and BASS, F

(1, 497) = .527, p > .05). Second, main effect results revealed no significant difference between group identification conditions (AE, F (1, 497) = .175, p > .05; AO, F (1, 497) = .750, p > .05; 97

OWP, F (1, 497) = .420, p > .05; SCW, F (1, 497) = .003, p > .05; and BASS, F (1, 497) = .012, p > .05). Finally, the interaction between body ideal profile pictures and group identification were not significant for any of the examined relationships (AE, F (1, 497) = .034, p > .05; AO, F

(1, 497) = 1.176, p > .05; OWP, F (1, 497) = .007, p > .05; SCW, F (1, 497) = .170, p > .05; and

BASS, F (1, 497) = .403, p > .05). Because there was no significant interaction effect for any of the five body image subscales, group identification was determined not to be a significant moderator variable. Hypothesis 6 was not supported.

RQ7: How does group identification affect the relationship between exposure to body ideal comments on Facebook and body image? H7 predicted that group identification would moderate the relationship between exposure to body ideal comments on Facebook and body image in that stronger group identification would result in more negative body image. Five factorial ANOVAs were conducted, each with friends’ comments as the independent variable, a body image subscale as the dependent variable, and group identification as the moderator variable (see Table 14). The moderator variable was treated as a second independent variable.

The analysis considered all possible interactions and main effects for the factors.

First, as reported in the H2 testing results, main effects results revealed no significant difference between the two comment conditions (AE, F (1, 497) = .551, p > .05; AO, F (1, 497)

= .570, p > .05; OWP, F (1, 497) = .831, p > .05; SCW, F (1, 497) = 2.342, p > .05; and BASS, F

(1, 497) = .956, p > .05). Second, main effect results revealed no significant difference between group identification conditions (AE, F (1, 497) = .215, p > .05; AO, F (1, 497) = .602, p > .05;

OWP, F (1, 497) = .342, p > .05; SCW, F (1, 497) = .030, p > .05; and BASS, F (1, 497) = .026, p > .05). Finally, the interaction between body ideal norm comments and group identification was not significant in any of the five analyses (AE, F (1, 497) = .407, p > .05; AO, F (1, 497) 98

= .234, p > .05; OWP, F (1, 497) = .883, p > .05; SCW, F (1, 497) = .058, p > .05; and BASS, F

(1, 497) = 2.121, p > .05). Because there was no significant interaction effect for any of the five body image subscales, group identification was determined not to be a significant moderator variable. Hypothesis 7 was not supported hence.

RQ8: Does exposure to body ideal profile pictures on Facebook affect participants’ body ideal norm comments? H8 predicted that exposure to body ideal profile pictures would prompt participants to make pro-ideal comments. Participants were more likely to generate a pro- ideal comment in the no body picture condition (88.9%) than the body ideal picture condition

(70.6%) (see Table 15). Conversely, participants were more likely to post an anti-ideal comment in the body ideal picture condition (29.4%) than the no body picture condition (11.1%). Results from a Pearson’s Chi-square test showed a significant difference in participants’ comments

2 between the body ideal picture condition and the no body picture condition, Χ = 20.725, p < .001.

Based on these results, hypothesis H8 was not supported and the reverse was observed.

RQ9: Does exposure to body ideal comments on Facebook affect participants’ body ideal norm comments? H9 predicted that exposure to body ideal comments would prompt participants to make pro-ideal comments. Participants were more likely to conform to the social norm, by making a pro-ideal comment in the pro-ideal comment condition (93.3%) than in the anti-ideal comment condition (62.4%) (see Table 16). Conversely, participants were more likely to go against the larger social norm, by posting an anti-ideal comment in the anti-ideal comment condition (37.6%) than in the pro-ideal comment condition (6.7%). Results from a Pearson’s

Chi-square test showed a significant difference in participants’ comments between the pro-ideal

2 comment condition and the anti-ideal comment condition, Χ = 59.490, p < .001. Based on these results, hypothesis H9 was supported. 99

RQ10: Is there an interaction effect between body ideal profile pictures on Facebook and body ideal comments on participants’ body ideal norm comments? H10 predicted that participants exposed to body ideal profile pictures and body ideal comments would make pro- ideal comments the most frequently. A Pearson’s Chi-square analysis was conducted to examine the interaction between the body ideal profile picture factor and the body ideal comment factor

(see Table 17). This analysis only compared participants’ comments that were pro-ideal.

Participants in the no body picture and pro-ideal comment condition were most likely to post pro-ideal comments (33.7%), followed by those in the body ideal picture and pro-ideal comment condition (31.6%), no body picture and anti-ideal comment condition (21.3%), and finally, body ideal picture and anti-ideal comment condition (13.4%). Although a discernable pattern emerged,

2 the interaction was not statistically significant, Χ = 2.692, p > .05. H10 was not supported.

RQ11: Does group identification affect the relationship between body ideal comments on Facebook and participants’ body ideal norm comments? H11 predicted that group identification would moderate the relationship between body ideal comments and participants’ pro-ideal comments in that stronger group identification would result in greater adherence to the body ideal norm. A Pearson’s Chi-square analysis was carried out between the comment conditions and group identification conditions (see Table 18). This analysis only compared participants’ comments that were pro-ideal. Participants in the pro-ideal comments and low group identification condition adhered to the group norm (41.4%) slightly more than those in the pro-ideal comments-high group identification condition (34.2%). There was no difference in the anti-ideal condition based upon the low (12.2%) and high (12.2%) group identification groups. Results showed no significant interaction effect between the comment 100

2 factor and group identification on commenting behavior, Χ = .468, p > .05. H11 was not supported.

RQ12: Are the effects of Facebook body ideal profile pictures on body image different between men and women? RQ12 asked if the effects of body ideal profile pictures on body image are different between men and women. Five factorial ANOVAs were conducted, each with body ideal profile pictures as the independent variable, a body image subscale as the dependent variable, and gender as the moderator variable (see Table 19).

First, main effect results revealed no significant difference between the two picture groups (AE, F (1, 497) = .087, p > .05; AO, F (1, 497) = .138, p > .05; OWP, F (1, 497) = .142, p > .05; SCW, F (1, 497) = .216, p > .05; and BASS, F (1, 497) = .478, p > .05). Second, main effect results for gender were examined. Women recorded significantly lower scores for the AE

(M = 3.32, SD = 0.81) and BASS (M = 3.38, SD = 0.74) than men did on AE (M = 3.54, SD =

0.69) and on BASS (M = 3.60, SD = 0.68), meaning women were less satisfied with their bodies than men. Also, men had lower scores on the measures of AO (M = 3.12, SD = 0.64) and OWP

(M = 2.22, SD = 0.87) than women did on AO (M = 3.48, SD = 0.60) and on OWP (M = 2.76, SD

= 0.97). Lower scores on these subscales means that men were less focused on their appearance and less preoccupied with their weight. Men also scored lower on the SCW (M = 3.02, SD =

0.73) subscale than women (M = 3.30, SD = 0.71), meaning they were less likely to perceive themselves as overweight than women. These differences were all statistically significant: AE, F

(1, 497) = 8.557, p < .01; AO, F (1, 497) = 37.503, p < .001; OWP, F (1, 497) = 37.893, p

< .001; SCW, F (1, 497) = 16.651, p < .001; and BASS, F (1, 497) = 11.131, p < .001. Last, the interaction between body ideal profile pictures and gender was not significant for any of the five body image measures: AE, F (1, 497) = .195, p > .05; AO, F (1, 497) = .020, p > .05; OWP, F (1, 101

497) = .093, p > .05; SCW, F (1, 497) = .173, p > .05; or BASS, F (1, 497) = .000, p > .05.

Because there was no significant interaction effect for any of the five body image subscales, gender was determined not to be a significant moderator variable.

RQ13: Are the effects of Facebook body ideal comments on body image different between men and women? RQ13 asked if the effects of body ideal norm comments on body image are different between men and women. Five factorial ANOVAs were conducted, each with body ideal comments as the independent variable, a body image subscale as the dependent variable, and gender as the moderator variable (see table 20).

First, main effects results revealed no significant difference between the two comment conditions: AE, F (1, 497) = .055, p > .05; AO, F (1, 497) = 1.569, p > .05; OWP, F (1, 497)

= .479, p > .05; SCW, F (1, 497) = 1.551, p > .05; BASS, F (1, 497) = .203, p > .05. Second, as noted in the results from H12, main effects results revealed significant differences between gender groups for all post-test body image subscales: AE, F (1, 497) = 8.640, p < .01; AO, F (1,

497) = 38.269, p < .001; OWP, F (1, 497) = 37.349, p < .001; SCW, F (1, 497) = 16.349, p

< .001; and BASS, F (1, 497) = 11.028, p < .001. Last, the interaction between body ideal profile pictures and predispositional body satisfaction was not significant for any of the five body image subscales: AE, F (1, 497) = 1.119, p > .05; AO, F (1, 497) = .677, p > .05; OWP, F (1,

497) = .163, p > .05; SCW, F (1, 497) = .244, p > .05; and BASS, F (1, 497) = 1.361, p > .001.

Because there was no significant interaction effect for any of the five body image subscales, gender was deemed not a significant moderator variable.

RQ14: Are the effects of Facebook body ideal profile pictures on participants’ pro- ideal comments different between men and women? RQ14 asked if the effects of body ideal profile pictures on pro-ideal comments are different between men and women. A Pearson’s Chi- 102 square analysis was run between the picture factor and gender. This analysis only compared participants’ comments that were pro-ideal (see Table 21).

Female participants in the no body picture condition were more likely to post pro-ideal comments (52.7%) than women in the body ideal picture condition (47.6%). Similarly, male participants in the no body picture condition were more likely to post pro-ideal comments

(59.8%) than men in the body ideal picture condition (40.2%). However, results showed no

2 significant interaction between gender and the body ideal picture factor, Χ = .1.581, p > .05.

RQ15: Are the effects of Facebook comments on participants’ body pro-ideal comments different between men and women? RQ15 asked if the effects of body ideal profile pictures on participants’ pro-ideal comments are different between men and women. A

Pearson’s Chi-square analysis was run between the comment factor and gender (see Table 22).

This analysis only compared participants’ comments that were pro-ideal. Female participants in the pro-ideal comment condition were more likely to post pro-ideal comments (66.7%) than women in the anti-ideal comment condition (33.3%). Similarly, male participants in the pro- ideal comment condition were more likely to post pro-ideal comments (61.7%) than men in the anti-ideal comment condition (38.3%). Results showed no significant differences between the

2 two comment conditions in pro-ideal comments when gender was introduced, Χ = .1.581, p > .05.

Post Hoc Supplementary Analyses

In addition to analyzing the variables associated with the main research questions and hypotheses, post hoc supplementary analyses were conducted. Specifically, Pearson product- moment correlation coefficients were obtained for the five MBSRQ subscales and Facebook usage patterns (see Table 23). Multiple pretest items were used to measure Facebook usage patterns. These items included the number of participants’ Facebook friends, years spent on 103

Facebook, and typical hours per day spent on Facebook. Also, Facebook activity measures were obtained for posting pictures of themselves, posting pictures of their friends, posting updates about themselves, and posting comments.

First, the appearance evaluation (AE) subscale was negatively linked to posting updates

(r = -.199, p < .001) and posting comments (r = -.165, p < .001). Those who posted updates and comments evaluated their appearance more negatively than those who did not post updates or comments. There were no significant relationships between AE and the number of Facebook friends (r = .048, p > .05), the number of years on Facebook (r = -.013, p > .05), the hours spent on Facebook per day (r = .009, p > .05), posting pictures of one’s self (r = -.080, p > .05), or posting pictures of one’s friends (r = -.074, p > .05).

The second subscale of the MBSRQ, appearance orientation (AO), also produced several significant correlations. AO was positively linked to the number of Facebook friends (r = .213, p

< .001), years on Facebook (r = .106, p < .05), posting pictures of the self (r = .335, p < .001), posting pictures of friends (r = .264, p < .001), posting updates (r = .130, p < .01), and posting comments (r = .130, p < .01). Participants who placed higher value on their appearance were more likely to have a high number of Facebook friends, to have spent more years on Facebook, to post pictures of themselves and their friends, to post updates and post comments than participants who did not focus as much on their outward appearance. AO was not significantly tied to typical daily hours spent on Facebook (r = .059, p > .05).

Third, overweight preoccupation (OWP) was significantly related to five of the seven

Facebook usage items. The significant Facebook usage items included the number of Facebook friends (r = .177, p < .001), posting pictures of self (r = .236, p < .001), posting pictures of friends (r = .256, p < .001), posting updates (r = .194, p < .001), and posting comments (r = .145, 104 p < .001). Participants who were more preoccupied with their weight were more likely to have a higher number of friends, post pictures, updates, and comments than those who were less preoccupied with their weight. There was no relationship between OWP and years on Facebook

(r = .076, p > .05) or daily hours spent on Facebook (r = .069, p > .05).

The fourth subscale, self-classified weight (SCW) was significantly related to three of the seven Facebook usage items, all of which were related to posting behavior: posting friends’ pictures (r = .194, p < .001), posting updates (r = .194, p < .001), and posting comments (r

= .194, p < .001). Those who felt they were overweight were more likely to post pictures of their friends, post updates and comments. There was no significant relationship between SCW and the number of Facebook friends (r = -.004, p > .05), years on Facebook (r = -.008, p > .05), daily hours spent on Facebook (r = .013, p > .05), or posting pictures of self (r = .086, p > .05).

The final MBSRQ subscale, body area satisfaction (BASS) was negatively tied to posting pictures of self (r = -.133, p < .001), posting pictures of friends (r = -.113, p < .001), posting updates (r = -.186, p < .001), and posting comments (r = -.151, p < .001). Those with lower body satisfaction were posting more pictures of themselves and friends, updates, and comments than those with higher body satisfaction. BASS was not significantly related to the number of

Facebook friends (r = .039, p > .05), years spent on Facebook (r = -.016, p > .05), or daily hours spent on Facebook (r = -.042, p > .05).

105

CHAPTER 5: DISCUSSION

This study was designed with six main objectives in mind: first, to expand the breadth of body image scholarship by investigating the effects of SNSs on body image; second, to broaden the scope of the SIDE model’s explanatory power by applying it to the SNS Facebook —a CMC environment that has yet to be examined from this perspective; third, to demonstrate that the consistent reinforcement of the body ideal norm has elevated it to societal or widespread status with the ability to affect behavior; fourth, to discover any gender differences in the effects; fifth, to gain insight for practical strategies that may be useful for those interested in ameliorating potentially negative effects of SNS usage on body image; and sixth, to develop a new, more realistic and externally valid experimental method. Each objective is discussed here in detail in relation to its’ corresponding research questions and hypotheses. In addition, for each objective, implications of the findings and areas for future inquiry are addressed.

Before moving on to each research question in detail, the Facebook usage results are addressed because they are deemed worthy of further discussion. Most students in the sample can be considered very active Facebook users. Over 90% of participants had logged on in the past 24 hours, and the average time spent on Facebook in a typical day was close to three hours.

Also, participants in the sample were typically not new to the SNS. The average number of years participants had been using Facebook was 4.32 (SD = 1.41). In addition, the most common

Facebook activities were posting comments, followed by personal updates, then pictures of themselves and their friends. Past research suggested that the ability to post comments and pictures are consistently appearing features of SNSs (boyd & Ellison, 2007); this finding indicates that these features may also be the most commonly used. The high frequency of 106 posting comments and pictures amongst participants found in this analysis serves to provide further justification for continued research that focuses on the impact of these features of SNSs.

Participants also reported having a relatively high number of friends when compared to previous studies on Facebook usage. Research on U.S. college students’ Facebook friend counts has shown an upward trend in the average number of friends. Early Facebook research reported the average friend count of college-aged participants to be between 150-250 friends (Ellison,

Steinfeld, & Lampe, 2007; Walther et al., 2008), whereas a more recent study reported an average of approximately 700 (McKinney, Kelly, & Duran, 2012). The latter number is fairly consistent with the finding here: The average number of friends reported was between the ranges of 451-600 friends and 601-850 friends. However, a recent survey of people over 18 reported that the average number of friends is 245 (Pew Internet and American Life Project, 2012).

Clearly, there is a discrepancy between the rising number college students’ Facebook friends and the stagnant average friend counts of U.S. adults. Although the average friend count is not of central importance to the current study, the findings here support the growing number of

Facebook friends among college students. Moreover, previous studies have demonstrated that friend counts were indicative of certain user-based characteristics. Antheunis and Schouten

(2011) found a connection between users’ friend counts and their perceived popularity. In addition, Lee et al. (2012) suggested that those with higher friend counts were more likely to display lower levels of self-esteem. Further research is warranted to investigate the meanings behind, motivation to accrue, and perception of high numbers of Facebook friends.

Objective 1: Body Image Research on SNSs

The first research question raised in this study explored the effects of exposure to

Facebook body ideal profile pictures on users’ body image. No significant differences emerged 107 between participants exposed to body ideal profile pictures and those exposed to no body profile pictures for any of the MBSRQ subscales. This finding suggests that there may be no main effect between these two variables. Also, these results do not corroborate with Haferkamp and

Kramer (2011) who found a significant relationship between exposure to attractive profile pictures and decreased body satisfaction. It is possible that differences in the experimental manipulation may have contributed to the diverging findings. First, the aforementioned study exposed participants to gender specific images only. Women were shown other women, and men were shown other men exclusively. The body ideal pictures of the opposite sex featured in the stimulus of the current study might have disrupted rather than elevated participants’ self- focused attention to their body. Second, the previous study exposed participants to four separate user profiles with large pictures, whereas the current analysis used a status page that featured all users at once with smaller pictures.

Because past research has demonstrated that exposure to idealized images of opposite sex as well as the same sex can have a negative effect on one’s body image (Aubrey & Taylor, 2009), it is unlikely that the mixed gender of the profile pictures resulted in the null finding. Moreover, exposure to same sex images only--at the exclusion of the opposite sex images--is a less externally valid manipulation of the SNS experience. Instead, the answer might be in the size of the body ideal pictures and the context in which they were presented. For instance, the current study used much smaller pictures than the previous experiment. Also, the current study displayed all pictures at once on a single Facebook status page while the previous experiment used four separate profile pages that each featured a single picture. The use of four unique profile pages may have enhanced the prominence of the profile pictures in each profile. More experiments on the effects of SNS members’ pictures on body image with various manipulations 108 of pictures and their contexts are needed to fully draw a solid conclusion about the relationship examined here.

The second research question looked at the effects of exposure to Facebook body ideal comments on users’ body image. As with body ideal pictures, no significant differences emerged between participants exposed to pro-ideal comments and those exposed to anti-ideal comments for all of the MBSRQ measures. While no other study has thus far examined the impact of SNS comments on body image, past research has shown that body image-related talk with friends or family members can lower body-esteem (Arroyo & Harwood, 2012; Krcmar,

Giles, & Helme, 2008; McCabe & Ricciardelli, 2001). Even though no significant difference between the two comment groups was observed, further experimental studies as well as the use of other methods such as survey and interview will be useful to help understand the role of SNS comments on body image.

The third research question explored the impact of the interaction between SNS body ideal pictures and body ideal comments on users’ body image. As with the main effects, the interaction between profile pictures and comments on body image were also not significant.

The fourth and fifth research questions involved the moderating effects of participants’ predispositional body satisfaction. Research question four concerned the role of participants’ predispositional body satisfaction in the relationship between exposure to SNS body ideal profile pictures and body image. Effects were found for three subscale measures of the MBSRQ that were directly related to body satisfaction. When participants held low predispositional body satisfaction, they were negatively affected by the ideal pictures in terms of their appearance evaluation (AE), body area satisfaction (BASS), and their overweight preoccupation (OWP); the opposite was the case for participants with high predispositional body satisfaction. Although not 109 all five subscales were subject to the interaction effect, the three that reached significance all concern attitudes about one’s body or anxiety about being fat. The two subscales that did not approach significance, AO and SCW, are not as directly related to body satisfaction as the others.

AO measures participants’ focus on their appearance, not their level of satisfaction with their appearance. Similarly, SCW simply asks to rate their perceptions of their weight, whether under- or overweight. Indeed, Cash (2005) noted the distinction between the five subscales, and, in particular, the difference between AE and AO highlighted here. Therefore, the H4 results here demonstrate that predispositional body satisfaction does moderate the effects of body ideal pictures on body satisfaction, but not one’s focus on their appearance or their weight classification.

Research question five asked whether participants’ predispositional body satisfaction would affect the relationship between exposure to SNS body ideal comments and body image.

Because there were no significant interaction effects for any of the five MBSRQ subscales, it was determined that predispositional body satisfaction was not a significant moderator variable of the comment effect on body image.

Overall, the findings for the first five research questions suggest that exposure to

Facebook can play a role in decreased body satisfaction in certain situations. Specifically, the results demonstrated that those who have low predispositional body satisfaction are at a higher risk of low body satisfaction after being exposed to body ideal profile pictures. This finding represents an important discovery in that it serves to expand the current breadth of research from the media effects perspective by examining the effects of a relatively unexplored medium in

SNSs. That is, it showed that idealized images on SNSs can negatively impact one’s body satisfaction, just as past studies that employed idealized images in traditional media have 110 consistently found. Furthermore, this analysis showed that the negative effects on body image can be present when individuals are exposed to mediated peer images of the body ideal, not simply mediated images of models and actors.

The significant finding here may be used as a starting point for future research on the relationship between exposure to body ideal pictures on SNSs and body image. For instance,

SNSs are unique in that they expose not only users to pictures of their “friends” but also to traditional media images of the body ideal (through shared photos, external links, and advertisements). The confluence and abundance of these pictures and images in one online space may significantly impact users’ body image. A study that accounts for the individual and combined influences of these varied types of idealized images will serve to strengthen our comprehensive understanding of SNSs’ impact on body image.

Objective 2: The SIDE Model and SNSs Through Group Identification

The next set of research questions tested the SIDE model by examining the effects of group identification. Research question six concerned the impact of group identification on the relationship between exposure to body ideal profile pictures on Facebook and body image.

There were no significant interaction effects for all five MBSRQ subscales, confirming that group identification was not a significant moderator variable. The seventh research question also explored the impact of group identification. This question was focused on the impact of group identification on the relationship between exposure to body ideal comments on Facebook and body image. As with the previous finding, there were no interaction effects between group identification and body ideal comments on body image.

Further investigating the SIDE model, research question eleven asked if group identification affects the relationship between SNS body ideal comments and adherence to the 111 body ideal norm. Although participants were more likely to adhere to the group norm—whether in the pro- or anti-ideal comment condition—in the high identification group, the difference between the high identification group and the low identification group was not significant.

Therefore, the findings do not support the impact of group identification on participants’ adherence to the group norm.

One explanation for the null findings on the impact of group identification is the potential influence of the medium itself. Participants may have identified more so with being on

Facebook than they did with the University. Since participants had been using Facebook for over

4 years on average and logged on for close to three hours daily, and also since habit was the strongest motivation for their Facebook use, participants may have fostered a level of connection or familiarity to the site that was present in both the low and high identification groups. It is possible that this connection negated any effects on body image that identification with participants’ University may have had. In fact, participants may not have made the connection between University affiliation and the stimulus Facebook page. This possible explanation may be due, in part, to a weak group identification manipulation. Namely, a stronger group identification stimulus may have outweighed any impact of Facebook affiliation itself.

This argument, though, is precariously leaning toward a technological deterministic explanation for the lack of effects. This is not the author’s intent, however. A technologically deterministic view focuses on the power of the medium itself to affect users’ perceptions and behavior in specific ways, such as Kiesler and colleagues’ (1985) argument that CMC environments lead to disinhibition of their users. The argument put forth here is more in line with a social shaping approach to technology (Ellison et al., 2006; MacKenzie & Wajcman,

1985). That is, CMC technologies “shape and are shaped by social practices” (Ellison, et al., 112

2006, p. 417). Because college students so frequently use Facebook, it is not unrealistic that their familiarity with the site may trigger a certain level of connection to it, and that this bond may be more influential than other factors such as one’s level of university affiliation. While it can be argued that such a connection may be possible for other ritualized CMC environments such as logging into to one’s email account, never before have we had a technology that has also completely changed our conceptions of social networks and how we interact in them.

An alternative explanation may also exist. The lack of significant results of group identification may be due to the potentially competing influences of high group identification and reduced social cues on adherence to the norm in SNSs. That is, while it is possible that a higher level of group identification may have increased participants’ adherence to the group norm in the high identification condition, it is also possible that the reduced social cues in the low group identification condition may have comparably increased adherence to the group norm.

The first influence concerning the impact of higher group identification is in line with the prediction made in H11 and also with previous research on the impact of group identification on norm adherence. For example, Lee (2007b) found a positive relationship between participants’ level of group identification and behavior toward the norm.

The second potential influence, that of reduced social cues, may have increased adherence to the norm in the low identification condition. For instance, it may be that participants had less knowledge about the students at the Southern California University (low identification condition)

(i.e., less social cues to form their initial perceptions) than about the students at Bowling Green

State University (high identification condition), and thus were more reliant on the basic group norms (i.e., the body ideal norms) to guide their behavior. In the same study that established the relationship between group identification and norm conformity, the author (Lee, 2007b) also 113 found that participants with less individuating information were more likely to conform to group norms than those with more individuating information. Other studies using the SIDE model have also shown that reduced social cues may lead to greater adherence to the group norms

(Postmes & Spears, 1998).

The ability for these competing influences to work in tandem is possible due to the common features of SNSs. In fact, Tong and colleagues (2008) noted that the use of the SIDE model to explore Facebook and SNSs with similar features may help to tease out the nuances of communication and behavior change on SNSs. While the current study did not produce significant findings for the impact of group identification, further analyses using the SIDE model that control the influence of competing variables such as reduced social cues may help to better understand the power of social identity on SNSs.

Objective 3: The SIDE Model, Group Norms, and Societal Norms

The next three research questions focused on the strategic aspect of the SIDE model by investigating the effects of exposure to SNS body ideal pictures and comments on participants’ adherence to the norm. Research question eight explored the impact of exposure to SNS body ideal pictures on participants’ pro-ideal comments. Interestingly, the findings were opposite to the predicted direction. Participants were more likely to generate pro-ideal comments in the no body picture condition than the body ideal picture condition. In the same vein, participants were more likely to post an anti-ideal comment in the body ideal picture condition than in the no body picture condition.

This finding may have touched upon an existing counter-norm to the body ideal that may have been triggered by the profile owners when they called attention to their bodies that were already in line with the body ideal. Such an explanation may account for the finding opposite to 114 the prediction. Both the body ideal and no body picture conditions featured the same two posts from the mock profile owners. The female profile owner asked for tips to lose weight because she was feeling fat, and the male profile owner proclaimed his elation for successfully engaging in an exercise and diet regiment. In response to these posts, participants were almost three times more likely to comment against the body ideal norm when they viewed the ideal body pictures than when they viewed no bodies. Because participants may have felt that the profile owners already fit the body ideal, the anti-ideal responses to their posts may have been in line with participants’ pre-existing counter-norm. For instance, in response to the female profile owner’s post, many participants responded with negative comments such as “Stop fishing for complements, you already look great.” This explanation supports recent SIDE model research that focused on the importance of preconceived norms in CMC. For example, Lee (2007a) found that assigning arbitrary gender-based visual cues to group members led participants to use gender-based norms in their group discussion.

Research question nine investigated the effects of exposure to SNS body ideal comments on participants’ body ideal norm comments. As predicted, participants were significantly more likely to conform to the body ideal social norm in the pro-ideal comment condition than in the anti-ideal comment condition, as demonstrated by the higher percent of non-conforming comments in the anti-ideal comment condition than in the pro-ideal comment condition.

Approaching this question in another way, participants’ adherence to the group norm was also analyzed and results demonstrated that the group norm impacted participants’ adherence to the pro-ideal social norm. That is, those exposed to the anti-ideal comments were significantly more likely to conform to the anti-ideal norm than those exposed to pro-ideal comments. This finding supports the traditional theorem of the SIDE model that individuals tend to adhere to the group 115 norm in CMC settings. Moreover, this finding provides support and further development of more recent SIDE model findings on the impact of widespread, societal level norms on strategic behavior in CMC contexts. As past studies have shown, scholars should not ignore the influence of large-scale social norms in the process of viewing the impact of group norms on participants’ behavior (Flanagin et al, 2002; Lee, 2007a).

Also, the overwhelming adherence to pro-ideal comments on SNSs may provide insight into an area of group communication largely unexplored in the communication field. Arroyo and

Harwood (2012) noted that although the work by other social sciences has begun to address what the authors label fat talk, “…no communication scholars have studied this phenomenon, despite its clear connection to the interpersonal process” (p. 169). The authors also suggested that, “A communication perspective frames fat talk as a dynamic and collaborative process wherein weight issues are negotiated between people.” (p. 169). Indeed, further investigation of pro-ideal comments on SNSs may be a useful endeavor to advance the study of mediated interpersonal and group communication. One perspective that may shed some light on this issue is the expectancy violation model which was used recently to explore normative behavior on SNSs (McLaughlin

Vitak; 2011; Rycyna Champion & Kelly, 2009). This research has found that SNS users may be perceived less favorably or even deleted as friends if they violate the norm, and the offending posts may be deleted as well. Using this framework in addition to the strategic component of the

SIDE model may be a useful way to investigate the impact of body ideal norm expectations on

SNS users’ normative behavior. In addition, never before has the body image social norm been tested in a SIDE model study. The results here provide ample justification for future SIDE model research to go beyond the use of gender norms and to begin exploring other societal level norms as part of the experimental manipulation, such as those related to body image. 116

Research question ten was the third research question that explored the relationship between body ideal pictures and body ideal comments on users’ adherence to the social norm.

This time, the interaction between body ideal pictures and body ideal comments on normative behavior was addressed. Possibly due to the significant opposite effect found for the impact of pro-ideal pictures on adherence to the norm, the interaction effect between pictures and comments was not significant. However, and in line with early SIDE model research, participants were most likely to adhere to the body ideal social norm when exposed to no body pictures and pro-ideal comments. At the same time, participants were least likely to make a pro- ideal comment when exposed to pro-ideal pictures and anti-ideal comments. These results provide further support for the anonymity component of the SIDE model. Indeed, past research has shown that anonymity—via no picture displayed during CMC group discussion instead of a picture displayed of each group member—lead to greater adherence to the group norms (Postmes et al., 2001).

Moreover, the findings here provide further evidence against the equalization hypothesis— that individuals can interact more equally and freely in CMC because of the reduced impact of social norms (Dubrovsky, et al., 1991). The results from this analysis demonstrate the power of the body ideal norm to influence behavior on SNSs. Interesting, Dubrovsky, Kiesler and Sethna

(1991) discussed the equalization hypothesis in the context of anonymous CMC interaction.

That is, the lack of identifiability from reduced social cues provides the ideal context for more equal communication to occur between individuals. This is noteworthy, because the current analysis found that participants were actually more normative (pro-ideal) when they were exposed to no body pictures (i.e. reduced social cues).

Objective 4: Exploring Gender Differences 117

In line with past body image research, there were gender differences for all MBSRQ subscales. Women were significantly more concerned about their body image than men. These findings support past body image research that illustrated exacerbated bodily discontent held by women than men. This finding is interesting in light of the recent evidence that men are demonstrating greater concerns about their body image than they have in the past. This study shows that there is still a significant gender gap in body image.

The remaining four research questions were concerned with the moderating role of gender in the effects of body image profile pictures and comments. Research questions twelve and thirteen explored gender differences in the effects of SNS body ideal profile pictures on body image. In answering the two research questions, there were no significant interaction effects for the five body satisfaction subscales, and thus, gender was not a significant moderator variable. So, even though women had significantly lower scores on all body image measures, the introduction of pro-ideal profile pictures or comments did not affect them any more than they affected men.

The last two research questions involved gender differences in participants’ adherence to the body ideal norm. Specifically, research question fifteen asked if there were gender differences in the effects of SNS body ideal profile pictures on pro-ideal participant comments.

As previously discussed, both men and women were more likely to adhere to the body ideal norm in the no picture condition than the pro-ideal condition. However, there were no significant gender differences in adherence to the norm. The final research question concerned gender differences in the effects of SNS comments on pro-ideal participant comments. As with the picture factor, no significant gender differences emerged in the effects of pro-ideal comments on adherence to the norm. 118

Although there were no significant gender differences found in commenting behavior, women were nearly 8% more likely to adhere to the body ideal norm in the pro-ideal picture condition than men. They were also 5% more likely to post pro-ideal comments in the pro-ideal condition than men. Thus, further investigation of gender differences in commenting behavior may be useful to see if these differences persist or even become more pronounced in other contexts.

Objective 5: Practical Implications

The results here demonstrated that, when exposed to body ideal pictures on SNSs, individuals with lower levels of predispositional body satisfaction were negatively affected in their body satisfaction. In addition, this investigation demonstrated that participants readily endorsed pro-ideal comments when cued by others on SNSs. In fact, nearly 80% of all messages participants posted were pro-body ideal. Although one time exposure to pro-ideal comments did not generate a noticeably negative impact on body image in the current study, research has suggested that such talk lowers body satisfaction and may lead to depression (Arroyo &

Harwood, 2012).

Even though this study highlights the potentially negative effects of SNSs on body image, the findings also demonstrated ways to resist the body ideal norm on SNSs. First, if the norm of one’s SNSs group is set to resist the body ideal norm through users’ comments, some individuals will resist based on the power of the group norm. Those in the anti-ideal comment condition resisted the body ideal norm significantly more so than those in the pro-ideal condition. Second, in opposition to the hypothesized direction of influence, those exposed to body ideal pictures were actually more likely to go against the body ideal norm than those exposed to no body pictures. As previously discussed, this finding may have touched upon a growing counter-norm 119 to the body ideal that is increasingly present in society today. One possible explanation for the presence of a counter-norm is the mounting prominence of campaigns aimed at refocusing the body ideal, like the Dove Campaign for Real Beauty. Such campaigns may have triggered participants’ willingness to speak out against the body ideal norm when they were confronted with body ideal images. The results from this study also demonstrate that participants who spoke out in opposition of the body ideal norm did so in various ways. For instance, some responses were negative, as previously discussed. However, in addition to responses that reprimanded the profile owners for attempting to fish for compliments, some participants also voiced concern for the profile owners who already met the body ideal through their anti-ideal comments. This was evident though comments like: “you’re fine the way there are,” “You’re crazy [for wanting to lose weight], “You already look good,” and “Be careful dieting too much, it can be bad for you.”

One strategy then is to increase the salience of the counter-norm by continuing campaigns that stimulate resistance to the clearly powerful body ideal norm. By doing so, the counter-norm might be pushed further into the mainstream. In addition, campaign messages that promote concern rather than reprimand for those who strive to emulate the body ideal may help to foster positive counter-norm comments based on empathy, not rebuke. More research also needs to be conducted to measure the effectiveness of body image campaigns to affect discussion about the body ideal norm.

Objective 6: Implementation of a New Experimental Method

The final objective was to develop an experimental method for web-based research on

SNSs that closely resembled users’ actual experiences on these sites. This objective was achieved through the use of the web design software and script writing in HTML, CSS, and

JavaScript, as well as a server site that utilized MySQL and PHP. Using these design tools, a 120 mock Facebook status page was created that allowed participants to: view multiple profile pictures, observe original posts from profile owners and comments from friends, and interact with profile owners and their friends by leaving a comment. All of these features were closely in line with the design and layout of those on Facebook, including the size of pictures, the ability to enlarge pictures by scrolling over them, the way in which Facebook users leave comments, the organization and layout of the comment feature, and more. Also, this design allowed participants to view the stimulus at home, at their leisure, and on the device of their choice—as they would normally access their Facebook account. These specific aspects of the experimental design and implementation helped to achieve a much higher level of external validity than past research that has explored this topic.

This method is seldom used in communication research, but provides a unique tool for investigating causal relationships between variables of interest in CMC environments. The overall design process resulted in the production of a systematic, yet creative and flexible example of a web-based experiment. The web-design tools employed here can be used to create detailed simulations of an unlimited array of CMC contexts and manipulations, as well as the ability to easily track participants’ information from the pretest to their completion of the study.

In addition, this method allows for the recruitment of large samples, with the ability to easily randomize stimulus condition assignment. Another related benefit of the method employed here is its’ high cost effectiveness, compared to traditional laboratory experiments that may require much time and money (Kraut et al., 2003). The successful administration of this technique should provide a foundation for other researchers who seek to investigate SNSs by using an experimental design.

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Post Hoc Supplementary Analyses

Post Hoc supplementary analyses revealed connections between some Facebook activities and body satisfaction. Overall, posting updates and comments were two Facebook usage items that were significantly related to the five MBSRQ subscales. That is, those with lower appearance evaluation and body area satisfaction, a greater focus on their appearance and preoccupation with their weight, and those who felt they were overweight were more likely to post comments and updates than those who were more satisfied with their bodies. Posting pictures of one’s self and posting pictures of one’s friends were also linked to four of the five body image subscales, meaning participants with poor body image were actually more likely to post pictures of themselves and their friends than those with greater body satisfaction.

These correlational findings are interesting, and perhaps provide more questions than answers. This is especially true when also accounting for the result from H4, that exposure to body ideal profile pictures negatively affected participants’ body satisfaction in those with lower predispositional body satisfaction. The H4 finding demonstrated the potentially negative effects of viewing idealized bodies on Facebook. Yet, the correlations found here also showed that those with a more negative body image were more likely to post pictures, among other things, than those with more positive body image. Somewhat ironically, the very individuals susceptible to the potentially negative effects of body ideal pictures were also more likely to post pictures.

Two questions remain unanswered. One, what different types of content are individuals with poor body image likely to post through pictures, comments, and updates (Whether in line with the body ideal or not)? Two, what motivated these individuals to post more pictures, comments and updates than those with more positive perceptions of their body image? The social compensation hypothesis might provide insight to both of these questions. Indeed, 122 previous research has shown that those with negative perceptions of their bodies were more likely to alter their self-presentations online to appear more attractive (Valkenburg et al., 2005), which supports the idea that participants who reported that they posted more pictures were doing so to place themselves more in line with the body ideal.

Although the social compensation hypothesis is plausible, the causal direction of influence needs further examination. For instance, as the social compensation hypothesis suggested, were those who posted more pictures, comments, and updates motivated to do so in an attempt to compensation for a more negative body image? Or did the act of posting pictures, comments and updates led to deterioration of one’s body image? To answer the second question, self-discrepancy theory (SDT) is one perspective that may be useful (Ewell et al., 1996; Harrison,

2001). Research has suggested that individuals who perceive incompatibilities between how they actually look (actual self) and how they feel others think they should look (ought self) are more likely to develop disordered eating behavior (Harrison, 2001). If individuals are posting pictures, comments, and updates on SNSs in an attempt to present themselves in line with what they perceive as an expected body ideal norm, they may be inadvertently reinforcing or even increasing the gap between their actual and ought selves. Such a self-discrepancy may lead to poorer body image and disordered eating behavior. It is also possible that both of these potential explanations are valid, which would suggest a cyclical effect of negative reinforcement. That is,

SNS users with a negative body image may be more likely to alter their self-presentations online and, through an increasing body image self-discrepancy, these acts may further negatively impact their body image. At this point, more rigorous testing of the relationship between body image and posting behavior is necessary to draw any tenable conclusions.

Lastly, the number of years participants were Facebook members and participants’ time 123 spent on Facebook each day were relatively unimportant to their body satisfaction. Previous survey research on Facebook use amongst adolescent girls revealed these items to be significantly related to lower levels of body satisfaction and the development of eating disorders

(Latzer et al., 2011). However, Acar (2008) found no relationship between the size of the social network and body image, self-esteem or anxiety. Further investigation into the frequency and duration of time spent on SNSs on body image is needed to get a more comprehensive picture of this relationship. Longitudinal research may be useful to test these relationships. Indeed, such research can be used to make predictions about time spent on FB on body image, instead of relying on correlation data alone. In addition, previous longitudinal research has demonstrated that those with low predispositional body satisfaction had lower body satisfaction than those with high predispositional body satisfaction after a yearlong magazine subscription (Stice et al., 2001).

A similar inquiry on SNSs may be a logical next step for body image research. That is, exposure to the body ideal on SNSs over a period of time may reveal other SNS features, in addition to profile pictures, to have negative effects on body image.

Limitations

One limitation of this study was the representativeness of the sample to the University population as a whole. 33.3% participants were men, 65.9% were women, and 0.8% categorized themselves as other. The sample gender distribution is slightly skewed in favor of women when compared to the University student population of 48% men and 54% women (National Center for Education Statistics, 2012). However, both the mean age and ethnic distribution were fairly representative in light of the NCES (2012) data.

Another limitation was in the operationalization of group identification. Group identification was manipulated in three ways. Participants in the high group identification 124 condition received a script informing them that the profiles they will view are of current students at their university. They were also instructed to proceed through the experiment assuming these student profiles are their close friends. Third, students in the high identification condition were told that they were likely to be called upon to meet others who participated in the study in face- to-face focus group sessions to discuss their answers. Such a manipulation was intended to evoke a perceived “threat of social sanction”, which has been used in past SIDE research that investigates the strategic aspect of the model (Barreto & Ellemers, 2000; Ellemers et al., 1999;

Kim & Park, 2011; Spears et al., 2002). Conversely, participants in the low identification group were informed that the profiles are from students at California State Poly Pomona. They were further instructed to proceed through the experiment assuming the student profiles were their social acquaintances or individuals that they did not know well, and they received no prompt for a perceived social sanction. As reported in both the results and discussion chapters, group identification had no significant impact on any of the outcome variables of interest.

Even though, as previously discussed, there may have been competing influences that nullified any significant effects due to group identification, improving the manipulation of group identification may serve to enhance future research endeavors. Perhaps because of the universality of the body ideal norm, the University affiliation may not have been a strong enough trigger to alter one’s body ideal attitudes or behavior. That is, participants’ identification with their University may have had little impact on their thoughts about body ideal norms. It may have been possible that a more specific manipulation of group identification was required in order to see its impact on participants. For example, a stronger manipulation of group identification might be to use participants’ actual peer groups on campus (i.e. sports teams,

Greek Life, or other student clubs/organizations) or student-based groups more directly related to 125 body image (i.e. group fitness, running club, etc.) as the in-group instead of the University as a whole.

Another potential remedy for this shortcoming is to assess the level of group identification without including it as a specific manipulation. This would simplify the design by making it a 2 x 2 between factors analysis rather than a 2 x 2 x 2 design. Group identification could be measured and participants’ level of group identification could still serve as the moderating variable. Such a change may also increase the external validity because participants would be able to more naturally engage in the stimulus without receiving prompts to “pretend” the Facebook profiles are of their close friends or their social acquaintances. Pretesting the effects of any new design is highly recommended.

A final limitation, one common to all experiments, is the lack of external validity. Every attempt was made to increase external validity, from the design of the stimulus—that included a replica of the most popular SNS, Facebook, and the inclusion of actual comments made my

Facebook users in the posts and comments—to the lack of a central testing site (participants were able to access the stimulus at their leisure, from their own computer, smartphone, or tablet, etc.).

However, the very nature of the experimental method does have restrictions on external validity.

For instance, even though participants were instructed to act as if the profile owners were their friends, they were not people that they knew personally. Participants may have acted differently in the experimental setting than they would have if interacting with their actual friends. In addition, participants were relatively anonymous. As noted above, a threat of social sanction was provided such that participants in the high identification condition were informed that they might be called upon to meet with other participants to discuss their comments in a face-to-face setting. However, other than this manipulation, participants were completely anonymous to the 126 mock profile owners. The anonymity felt by participants may have contributed the lack of adherence to the group norms. Future investigations of SNSs may want to include anonymity as a separate factor, or to increase external validity, have all participants provide individuating information so that they perceive the interaction as more realistic and so they are held accountable for their behavior. This limitation may also account for the lack of significant main effects of exposure to pro-ideal pictures and comments.

Summary

Overall, the results demonstrated that pro-ideal pictures and comments on SNSs can impact users’ body satisfaction and their behavior in adherence to the body ideal norm.

Although the main factor effects of SNS pictures and comments on body satisfaction were not significant, effects were found when accounting for participants’ predispositional body satisfaction. That is, those with low predispositional body satisfaction were more affected by pro-ideal pictures on several body image subscales than those with high predispositional body satisfaction. Moreover, this study demonstrated the overwhelming power of the body ideal norm to influence Facebook users’ behavior. As past research has suggested, endorsing the body ideal during group discussion can negatively impact one’s body image.

In addition, and in support of the SIDE model, the impact of body ideal comments on participants’ comments was significant. Even though the majority of participants conformed to the social norm of the body ideal, they were significantly less likely to do so when exposed to anti-ideal comments. This finding showed support for the basic tenant of the SIDE model that communication online tends to be normative in SNSs, a CMC environment yet to be examined from this theoretical perspective prior to the current investigation. Furthermore, and in support of more recent SIDE model research, this finding also demonstrated the power of the body ideal 127 norm on a societal level to influence behavior. Interestingly, group identification failed to play a significant role. The results showed that group identification did not affect the relationship between exposure to body ideal pictures and comments and body image, nor did it affect the relationship between exposure to pictures and comments and adherence to the norm. For this finding, the particular aspect of SIDE model was not supported. In addition, because participants’ comments were significantly influenced by both social level and group level norms, and due to the discovery of a potential counter-ideal norm, this study does have implications for the potential ways to increase the resistance to the body ideal norm. Moreover, the significant correlations found in the Post Hoc supplementary analysis raise important questions about the relationship between body image and posting behavior on SNSs. Finally, the experimental design employed here exemplifies a new and unique approach to communication inquiry with potentially limitless future research possibilities. Altogether, the findings from this study demonstrate the importance of continued body image research in SNSs as well as the applicability of the SIDE model in this new CMC context. In addition, the findings encourage further research on body image that is guided by the SIDE model.

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REFERENCES

Acar, A. (2008). Antecedents and consequences of online social networking behavior: The case

of Facebook. Journal of Website Promotion, 3, 62-83. DOI:10.1080/15533610802052654

Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information sharing, and

privacy on the Facebook. In P. Golle & G. Danezis (Eds.), Proceedings of 6th Workshop

on Privacy Enhancing Technologies (pp. 36–58). Cambridge, UK: Robinson College.

Agliata, D., & Tantleff-Dunn, S. (2004). The impact of media exposure on males’ body image.

Journal of Social & Clinical Psychology, 23(1), 7-22.

Ahn, J. (2012). Teenagers’ Experiences With Social Network Sites: Relationships to Bridging

and Bonding Social Capital. Information Society, 28(2), 99-109.

doi:10.1080/01972243.2011.649394

Allport, G. (1924). The study of undivided personality. Journal of Abnormal Psychology

& Social Psychology, 19, 132 – 141.

Andersen, A. E., & DiDomenico, L. (1992). Diet vs. shape content of popular male and female

magazines: A dose-response relationship to the incidence of eating disorders?

International Journal of Eating Disorders, 11(3), 283-287.

Antheunis, M. L., & Schouten, A. P. (2011). The Effects of Other-Generated and System-

Generated Cues on Adolescents' Perceived Attractiveness on Social Network Sites.

Journal Of Computer-Mediated Communication, 16(3), 391-406. doi:10.1111/j.1083-

6101.2011.01545.x

Arbour, K. P., & Ginis, K. A. M. (2006). Effects of exposure to muscular and

hyper muscular images on young men’s muscularity dissatisfaction and body

dissatisfaction. Body Image, 3, 151-161. 129

Aronson, E., & Carlsmith, J. M. (1968). Experimentation in social psychology. In G.

Lindzey and E. Aronson (Eds.), The handbook of social psychology (2nd ed.). Reading,

MA: Addison-Wesley.

Arroyo, A., & Harwood, J. (2012). Exploring the Causes and Consequences of Engaging in Fat

Talk. Journal Of Applied Communication Research, 40(2), 167-187.

doi:10.1080/00909882.2012.654500

Asch, S. E. (1956) Studies of independence and conformity: A minority of one against a

unanimous majority. Psychological Monographs, 70 (Whole no. 416).

Aubrey, J. S., & Taylor, L. D. (2009). The role of lad magazines in priming men’s

chronic and temporary appearance-related schemata: An investigation of longitudinal and

experimental findings. Human Communication Research, 35, 28-58.

Barreto, M., & Ellemers, N. (2000). You can’t always do what you want: Social identity

and self-presentational determinants of the choice to work for a low-status group.

Personality and Social Psychology Bulletin, 26(10), 891-906.

Bahrke, M. S., Yesalis, C. E., & Brower, K. J. (1998). Anabolic androgenic steroid abuse and

performance-enhancing drugs among adolescents. Child and Adolescent Psychiatric

Clinics of North America, 7, 821– 838.

Barabasi, A. (2003). Linked: How everything is connected to everything else and what it means

for business, science, and everyday life. New York: Penguin Group.

Barlett, C. P., Vowels, C. L., & Saucier, D. A. (2008). Meta-analysis on the effects of

media images on men’s body-image concerns. Journal of Social and Clinical

Psychology, 27(3), 279-310. 130

Barnard, A. (September, 2007). New York investigating Facebook’s safety rules. The New York

Times. Retrieved from: www.nytimes.com.

Barnes, S. (2006). A privacy paradox: Social networking in the United States. First Monday,

11(9). Retrieved July 2, 2012 from http://www.firstmonday.org/issues/issue11_9/

barnes/index.html

Barlett, C. P., Vowels, C. V., & Saucier, D. A. (2008). Meta-analyses of the effects of media

images on men's body-image concerns. Journal of Social and Clinical Psychology, 27,

279- 310.

Beasley, B. & Collins Standley, T. (2002). Shirts vs. skins: Clothing as an indicator of gender role

stereotyping in video games. Mass Communication and Society, 5(3), 279-293.

Beer. D. (2008). Social network(ing) sites…revisiting the story so far: A response to dana boyd

& Nicole Ellison. Journal of Computer-mediated Communication, 13, 516-529.

Bell, C. (2008) Bigger, Stronger, Faster: The Side Effects of Being American [Documentary].

United States: Magnolia.

Berger, A. A. (2011). Media and communication research methods, 2nd ed. Los Angeles: Sage.

Berscheid, E., Walster, E., & Bohmstedt, G. (1973). The happy American body: A survey report.

Psychology Today, 7(6), 119-131.

Boase, J. Horrigan, J. B., Wellman, B. & Rainie, L. (2006). The strength of internet ties, Pew

Internet & American Life Project. Retrieved July, 1, 2012, from

http://www.pewinternet.org/pdfs/PIP_Internet_ties.pdf

Botta, R. A. (1999).Television images and adolescent girls’ body image disturbance. Journal of

Communication,49, 22-41.

Botta, R. A. (2003). For your health? The relationship between magazine reading and 131

adolescents’ body image and eating disturbances. Sex Roles, 48, 389 –399. boyd, d. (2004). Friendster and publicly articulated social networks. Proceedings of ACM

Conference on Human Factors in Computing Systems (pp. 1279–1282). New York: ACM

Press. boyd, d. (2006). Friends, Friendsters, and MySpace Top 8: Writing community into being on

social network sites. First Monday, 11(12). Retrieved July 21, 2007 from http://

www.firstmonday.org/issues/issue11_12/boyd/ boyd, d. (2007). Why youth (heart) social network sites: The role of networked publics in

teenage social life. In D. Buckingham (Ed.), Youth, Identity, and Digital Media (pp. 119–

142). Cambridge, MA: MIT Press. boyd, d., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship.

Journal of Computer-Mediated Communication, 13, 210-230. boyd, d., & Heer, J. (2006). Profiles as conversation: Networked identity performance on

Friendster. In Proceedings of the Thirty-Ninth Annual Hawai’i International Conference

on System Sciences. Los Alamitos, CA: IEEE Press.

Brym, R. J., & Lenton, R. L. (2001). Love Online: A Report on Digital Dating in Canada.

Retrieved January 5, 2012, from

http://www.nelson.com/nelson/harcourt/sociology/newsociety3e/loveonline.pdf

Buffardi, L. E., & Campbell, W. K. (2008). Narcissism and social networking web sites.

Personality and Social Psychology Bulletin, 34(10), 1303–1314.

Caplan, S. E. (2003). Preference for Online Social Interaction: A Theory of Problematic Internet

Use and Psychosocial Well-Being. Communication Research, 30(6), 625-648.

doi:10.1177/0093650203257842 132

Cash, T. F. (1997). The body image workbook: An 8-step program for learning to like your looks.

Oakland, CA: New Harbinger.

Cash, T.F. (2000). Body image. In A. Kazdin (Ed.), The Encyclopedia of Psychology (pp. 436-

439). Washington, D.C: American Psychological Association and Oxford University

Press.

Cash, T.F. (2002). Body image: Cognitive behavioral perspectives on body image. In T.F. Cash

& T. Pruzinsky (Eds.), Body Images: A Handbook of Theory, Research, and Clinical

Practice (pp. 38-46). NY: Guilford Press.

Cash, T. F., Winstead, B. A., & Janda, L. H. (1986). The great American shape-up. Psychology

Today, 24(1), 30-37.

Chalfen, R. (2002). Snapshots "r" us: the evidentiary problematic of home media. Visual

Studies, 17, 141-149.

Chan, M. (2010) The Impact of E-mail on Collective Action: A Field Application of the

SIDE Model. New Media & Society, 12(8), 1313-1330.

Coleman, J. S. (1988). Social capital in the creation of human capital. The American Journal of

Sociology, 94(Supplement): S95-S120. Cook, T. D., Cook, D. T., &

Debatin, B., Lovejoy, J., Horn, A., & Hughes, B. (2009). Facebook and Online Privacy:

Attitudes, Behaviors, and Unintended Consequences. Journal of Computer-Mediated

Communication, 15, 83-108. doi:10.1111/j.1083-6101.2009.01494.x

Deutsch, M. & Gerard, H. B. (1955) A study of normative and informational social

influence upon individual judgment, Journal of Abnormal and Social Psychology, 51, 629-

636.

Diener, E. (1977). Deindividuation: Causes and Consequences. Social Behavior & Personality: 133

An International Journal, 5(1), 143. Retrieved from EBSCOhost.

Diener, E., & Wallbom, M. (1976). Effects of self-awareness on antinormative behavior .

Journal of Research in Personality, 10, 107-111

Diener, E. (1980). Deindividuation: The absence of self-awareness and self-regulation in group

members. In P. B. Paulus (Ed.), The psychology of group influence (pp. 209–242).

Hillsdale, NJ: Lawrence Erlbaum.

Dill, K. E. & Thill, K. P. (2007). Video game characters and socialization of gender roles: Young

people’s perceptions mirror sexist media depictions. Sex Roles, 57(11/12), 851-864.

Dietz, T. L. (1998). An examination of violence and gender role portrayals in video games:

Implications for gender socialization and aggressive behavior. Sex Roles, 38(5/6), 425-442.

DiMicco, J. M., Millen, D. R. (2007). Identity management: multiple presentations of self

in Facebook. In: Proceedings of the 2007 ACM conference on supporting group work,

ACM Press, Sanibel Island, pp. 383–386.

Donath, J. (2007). Signals in social super nets. Journal of Computer-Mediated Communication,

13(1), article 12.

Donath, J., & boyd, d. (2004). Public displays of connection. BT Technology Journal, 22(4),

71–82.

Dubrovsky, V.J., Kiesler, S., & Sethna, B.N. (1991). The equalization phenomenon: Status

effects in computer-mediated and face-to-face decision-making groups. Human

Computer Interaction,6,119-146.

Dwyer, C., Hiltz, S. R., & Passerini, K. (2007). Trust and privacy concern within social

networking sites: A comparison of Facebook and MySpace. Proceedings of AMCIS

2007, Keystone, CO. Retrieved February 2, 2012 from 134

http://csis.pace.edu/~dwyer/research/DwyerAMCIS2007.pdf

Ellemers, N., van Dyck, C., Hinkle, S., & Jacobs, A. (2000). Intergroup differentiation in social

context: Identity needs versus audience constraints. Social Psychology Quarterly, 63(1),

60-74.

Ellison, N., Heino, R., & Gibbs, J. (2006). Managing impressions online: Self-presentation

processes in the online dating environment. Journal of Computer-Mediated

Communication, 11(2), article 2.

Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook "friends:" Social

capital and college students' use of online social network sites. Journal of Computer-

Mediated Communication, 12(4), article 1. Retrieved from:

http://jcmc.indiana.edu/vol12/issue4/ellison.html.

Ewell , F., Smith, S., Karmel, M. P., & Hart, D. (1996). The sense of self and its development: A

framework for understanding eating disorders. In L. Smolak, M. P. Levine, & R. Striegel-

Moor e ( Eds. ), The developmental psychopathology of eating disorders : Implications

for research, prevention, and treatment (pp. 107-133). Mahwah, NJ: Erlbaum.

Festinger, L., Pepitone, A., Newcomb, T. (1952). Some consequences of de-individuation in a

group. Journal of Abnormal and Social Psychology, 47, 382-389.

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7, 117-140.

Field, A, E., Carmago, C. A., Jr, Taylor, C. B., Berkey, C. S., & Colditz, G. A. (1999). Relation

of peer and media influences to the development of purging behaviors among

preadolescent and adolescent girls. Archives of Pediatric and Adolescent Medicine, 153,

1184–1189.

Flanagin, A. J., Tiyaamornwong, V., O'Connor, J., & Seibold, D. R. (2002). Computer-Mediated 135

Group Work: The Interaction of Sex and Anonymity. Communication Research, 29(1),

66. Retrieved from EBSCOhost.

Frederick, D. A., Fessler, D. M., & Haselton, M. G. (2005). Do representations of male

muscularity differ in men’s and women’s magazines? Body Image, 2, 81-86.

Friedlander, L. (2011). Friending the Virgin: Some Thoughts on the Prehistory of

Facebook. SAGE Open, DOI: 10.1177/2158244011415423

Garner, D. M., Garfinkel, P. E., Schwartz, D., & Thompson, M. (1980). Cultural expectation of

thinness in women. Psychological Reports, 47, 483-491.

Gaudio, R., & Taplin, D. C. (2005) Shredded [Documentary]. Canada: National Film Board of

Canada.

Goffman (1959). The presentation of self in everyday life. New York: Doubleday.

Gonzales, A. L., & Hancock, J. T. (2011). Mirror, Mirror on my Facebook Wall: Effects of

Exposure to Facebook on Self-Esteem. CyberPsychology, Behavior & Social Networking,

14(1/2), 79-83. doi:10.1089/cyber.2009.0411

Gonzales, A. L., & Hancock, J. T. (2008). Identity shift in computer-mediated environments.

Media Psychology, 11, 167-185.

Gordon, J. (February, 2006). MySpace draws a questionable crowd. The New York Times,

Retrieved from www.lexisnexis.com/hottopics/lnacademic.

Govani, T., & Pashley, H. (2005). Student awareness of the privacy implications when using

Facebook. Carnegie Mellon. Retrieved May 5, 2007, from

http://lorrie.cranor.org/courses/fa05/tubzhlp.pdf

Grabe, S., Ward, L. M., & Hyde, J., S. (2008). The role of the media in body image 136

concerns among women: A meta-analysis of experimental and correlational studies.

Psychological Bulletin, 134(3), 460-476.

Grogan, S., Williams, Z., & Conner, M. (1996). The effects of viewing same gender

photographic models on body satisfaction. Women and Psychology Quarterly, 20, 569-

575.

Greenberg, B. S., Eastin, M., Hofschire, L., Lachlan, K., & Brownell, K. D. (2003). Portrayals of

Overweight and Obese Individuals on Commercial Television. American Journal of

Public Health, 93(8), 1342-1348. Retrieved from EBSCOhost.

Groesz, L. M., Levine, M. P., & Murnen, S. K. (2002). The effect of experimental presentation

of thin media images on body satisfaction: A meta-analytic review. International Journal

of Eating Disorders, 31, 1-16.

Gross, R., Acquisti, A. & Heinz, H. J. III. (2005). Information revelation and privacy in online

social networks. ACM workshop on Privacy in the Electronic Society, New York, 71–80.

Gunther, A. C., & Storey, J. D. (2003). The influence of presumed influence. Journal of

Communication,53(2),199-224.

Haferkamp, N., & Kramer, N. (2011). Social Comparison 2.0: Examining the Effects of Online

Profiles on Social-Networking Sites. Cyberpsychology, Behavior, and Social Networking,

14(5), 309-14.

Halliwell, E., Dittmar, H., & Osborn, A. (2007). The effects of exposure to muscular male

models among men: Exploring the moderating role of gym use and exercise motivation.

Body Image, 4, 278-287.

Hampton, K., & Wellman, B. (2003). Neighboring in Netville: How the Internet supports

community and social capital in a wired suburb. City & Community, 2 (4), 277-311. 137

Hancock, J. T. & Toma, C. L. (2009). Putting your best face forward: The accuracy of online

dating profile photographs. Journal of Communication, 59, 367-386.

Hargittai, E., & Hsieh, Y. P. (2011). From dabblers to omnivores: A typology of social

network site usage, In: Z. Papacharissi (Ed.), A networked self: Identity, community and

culture on social network sites. New York: Routledge, pp. 146–168.

Harrison, K. (1997). Does interpersonal attraction to thin media personalities promote

eating disorders? Journal of Broadcasting and Electronic Media, 41, 478-500.

Harrison, K. (2000). Television viewing, fat stereotyping, body shape standards, and eating

disorder symptomatology in grade school children. Communication Research, 27(5),

617-640.

Harrison, K. (2001). Ourselves, our bodies: Thin-ideal media, self-discrepancies, and eating

disorder symptomatology in adolescents. Journal of Social and Clinical Psychology, 20,

289-323.

Harrison, K., & Bond, B., J. (2007). Gaming magazines and the drive for muscularity in

preadolescent boys: A longitudinal study. Body Image, 4, 269-277.

Harrison, K., & Cantor, J. (1997). The relationship between media exposure and eating disorders.

Journal of Communication,47,40-67.

Harrison, K., & Fredrickson, B. L. (2003). Women's Sports Media, Self-Objectification, and

Mental Health in Black and White Adolescent Females. Journal of Communication,

53(2), 216-232. Retrieved from EBSCOhost.

Harrison, K., Taylor, L. D., & Marske, A. L. (2006). Women’s and men’s eating behavior

following exposure to ideal-body images and text. Communication Research, 33(6), 507-

529. 138

Hausenblas, H. A., Janelle, C. M., Gardner, R. E., & Hagan, A. L. (2002). Effects of exposure to

physique slides on the emotional responses of men and women. Sex Roles, 47, 569–575.

Heer, J., & boyd, d. (2005). Vizster: Visualizing online social networks.

IEEE Proceedings of Symposium on Information Visualization (InfoVis 2005). (pp. 33-

40). IEEE Press. Minneapolis, Minnesota, October 23-25.

Heinberg, L. J., & Thompson, J. K. (1992). Social comparison: Gender, target importance ratings

and relation to body image disturbance. Journal of Social Behavior and Personality, 7,

335–344.

Heinberg, L. J., & Thompson, J. K. (1995). Body image and televised images of thinness and

attractiveness: A controlled laboratory investigation. Journal of Social and Clinical

Psychology, 14, 325-338.

Henderson-King, D., Henderson-King, E., & Hoffman, L. (2001). Media images and women's

self-evaluations: Social context and importance of attractiveness as moderators.

Personality and Social Psychology Bulletin, 11, 1407-1416.

Hozba, C. L., Walker, K. E., Yakushko, O., & Peugh, J. L. (2007). What about men? Social

comparison and the effects of media images on body and self-esteem. Psychology of men

& Masculinity, 8(3), 161-172.

Hogg, M. A., & Reid, S. A. (2006). Social identity, self-categorization, and the communication

of group norms. Communication Theory, 16, 7-30.

Hogg, M. A., & Tindale, R. S. (2005). Social identity, influence, and communication in

small groups. In J. Harwood & H. Giles (Eds.), Intergroup communication:

Multiple perspectives (pp. 141-164). New York: Peter Lang.

Hogg, M. A., & Turner, J. C. (1987). Social identity and conformity: A theory of referent 139

informational influence. In W. Doise & S. Moscovici (Eds.), Current issues in European

social psychology (Vol. 2, pp. 139-182). Cambridge, UK: Cambridge University Press.

Hopkins, N., Reicher, S., Harrison, K., Cassidy, C, Bull, R., & Levine, M. (2007). Helping to

improve the group stereotype: On the strategic dimension of pro-social behavior.

Personality and Social Psychology Bulletin, 33, 776-788.

Irving, L. M. (1990). Mirror images: Effects of the standard of beauty on the self- and body-

esteem of women exhibiting varying levels of bulimic symptoms. Journal of Social and

Clinical Psychology, 9, 230-242.

Isenberg, Daniel J. (1986), "Group Polarization: A Critical Review and Meta-Analysis,"

Psychological Bulletin, 50(6), 1141-1151.

Jenkins, H. (2006): Confronting the Challenges of Participatory Culture: Media Education for the

21st Century. Retrieved on July 3, 2012, from: http://www.digitallearning.macfound.org/.

Jetten, J., Spears, R., Manstead, A. S. R. (1997). Strength of identification and intergroup

differentiation; The influence of group norms. European Journal of Social Psychology,

27, 816.1-816.7.

Kalodner, C. R. (1997). Media influences on male and female non-eating-disordered college

students: A significant issue. Eating Disorders: The Journal of Treatment and Prevention,

5, 47-57.

Kaplan, M. F. (1987). The influence process in group decision making. In C. Hendrick

(Ed.), Review of personality and social psychology: Group processes (Vol. 8, pp. 189-

212). Beverly Hills, CA: Sage.

Kavanaugh, A., Carroll, J. M., Rosson, M. B., Zin, T. T., and Reese, D. D. (2005). 140

Community networks: Where offline communities meet online. Journal of Computer-

Mediated Communication, 10(4), article 3.

Kelly, C. (1993). Group identification, intergroup perceptions and collective action. European

Review of Social Psychology, 4, 59-83.

Kerlinger, F. N., & Lee, H., B. (2000). Foundations of behavioral research, 4th ed. New

York: Harcourt College Publishers.

Kiesler, S., Siegel, J., & McGuire, T. (1984). Social psychology aspects of computer-

mediated communication. American Psychologist, 39, 1123-1134.

Kiesler, S., Zubrow, D., Moses, A. M., & Geller, V. (1985). Affect in computer-mediated

communication: An experiment in synchronous terminal-to-terminal discussion. Human

Computer Interaction, 1, 77-104.

Killbourne, J. (Director). (1995) Slim hopes: Advertising & the obsession with thinness

[Documentary]. United States: Media Education Foundation.

Killbourne, J. (Director). (2000; 2010) Killing us softly: Advertising’s image of women

[Documentary]. United States: Media Education Foundation.

Kim, J., & Parks, H. (2011). The effect of uniform virtual appearance on conformity intention:

Social identity model of deindividuation effects and optimal distinctiveness theory.

Computers In Human Behavior, 27(3), 1223-1230. doi:10.1016/j.chb.2011.01.002

Klein, O., Spears, R., & Reicher, S. (2007). Social Identity Performance: Extending the

Strategic Side of SIDE. Personality & Social Psychology Review (Sage Publications Inc.),

11(1), 28-45. doi:10.1177/1088868306294588

Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002).

Internet paradox revisited. Journal of Social Issues, 58 (1), 49-74. 141

Kraut, R., Olson, J., Banaji, M., Bruckman, A., Cohen, J., & Couper, M. (2003). Phychological

Research Online: Opportunities and Challenges.

Kraut, R., Patterson, M., Lundmark, V., et al. (1998). Internet paradox: a social technology that

reduces social involvement and psychological well being? American Psychologist

53:1017–1031.

Krcmar, M., Giles, S., & Helme, D. (2008). Understanding the Process: How Mediated and

Peer Norms Affect Young Women's Body Esteem. Communication Quarterly, 56(2),

111-130. doi:10.1080/01463370802031844

Kuczmarski, R. J., Flegal , K. M., Campbell, S. M., & Johnson, C. L. (1994). Increasing

prevalence of overweight among US adults. Journal of the American Medical Association,

272, 205-211.

Kugihara, N. (2001). Effects of aggressive behaviour and group size on collective escape in

an emergency: A test between a social identity model and deindividuation theory. British

Journal Of Social Psychology, 40(4), 575.

Kumar, R., Novak, J., & Tomkins, A. (2006). Structure and evolution of online social

networks. Proceedings of 12th International Conference on Knowledge Discovery in

Data Mining (pp. 611–617). New York: ACM Press.

Labre, M. P. (2005). Burn fat, build muscle: A content analysis of Men’s Health

and Men’s Fitness. International Journal of Men’s Health, 4, 187-200.

Lampe, C., Ellison, N., & Steinfield, C., (2006). A Face(book) in the crowd: Social

searching vs. social browsing. Proceedings of the 2006 20th Anniversary Conference on

Computer Supported Cooperative Work (pp. 167-170). New York: ACM Press.

Lapinski M. K., & Rimal, R. N. (2005). An Explication of Social Norms. 142

Communication Theory, 15(2), 127-147.

LaRose, R., & Eastin, M.S. (2004). A social cognitive theory of Internet uses and gratifications:

Toward a new model of media attendance," Journal of Broadcasting & Electronic Media,

48(3), 358-377.

Latzer, Y., Katz, R., & Zohar, S. (2011). The relationship between disordered eating and

body image, empowerment and exposure to media messages, among female adolescents

in Israel. Unpublished Manuscript.

Lautman, M. (1991). End-benefit segmentation and prototypical bonding. Journal of Advertising

Research, 31(3), 9-18.

Lavine, H., Sweeney, D., & Wagner, S. H. (1999). Depicting women as sex objects in television

advertising: Effects on body dissatisfaction. Personality and Social Psychology Bulletin,

25, 1049-1058.

Law, C., & Labre, M. P. (2002). Cultural Standards of Attractiveness: A thirty-year look at

changes in male images in magazines. Journalism & Mass Communication Quarterly,

79(3), 697-711.

Lea, M., & Spears, R. (1991). Computer-mediated communication, deindividuation,

and group decision-making. International Journal of Man Machine Studies, 34, 283-301.

Lea, M., & Spears R. (1992). Paralanguage and social perception in computer-mediated

communication. Journal of Organizational Computing, 2, 321–341

Lea, M., Spears, R., & de Groot, D. (2001). Knowing me, knowing you: Anonymity effects on

social identity processes within groups. Personality and Social Psychology Bulletin, 27,

526-537.

Le Bon, G. (1895). The Crowd: A Study of the Popular Mind. London: Transaction 143

Publishers.

Lee, E. (2004). Effects of visual representation on social influence in computer-mediated

communication experimental tests of the social identity model of deindividuation effects.

Human Communication Research, 30(2), 234-259.

Lee, E. (2006). When and how does depersonalization increase conformity to group norms in

computer-mediated communication? Communication Research, 33(6), 423-447.

Lee, E. (2007a). Categorical person perception in computer-mediated communication: Effects of

character representation and knowledge bias on sex inference and informational social

influence. Media Psychology, 9(2), 309-329. doi:10.1080/15213260701286007.

Lee, E. (2007b). Deindividuation Effects on Group Polarization in Computer-Mediated

Communication: The Role of Group Identification, Public-Self-Awareness, and

Perceived Argument Quality. Journal of Communication, 57(2), 385-403.

doi:10.1111/j.1460-2466.2007.00348.x

Lee, J., Moore, D., Park, E., & Park, S. (2012). Who wants to be “friend-rich”? Social

compensatory friending on Facebook and the moderating role of public self-

consciousness. Computers In Human Behavior, 28(3), 1036-1043.

doi:10.1016/j.chb.2012.01.006

Leit, R., A., Gray, J. J., & Pope, H. G., Jr. (2002). The media’s representation of

the ideal male body: A cause for muscle dysmorphia? International

Journal of Eating Disorders, 31, 334-338.

Leit, R. A., Pope, H. G., & Gray, J. J. (2001). Cultural expectations of muscularity

in men: The evolution of playgirl centerfolds. International Journal of Eating Disorders,

29, 90-93. 144

Lenhart, A., & Madden, M. (2007). Teens, Privacy, and Online Social Networks: How Teens

Manage Their Online Identities and Personal Information in the Age of MySpace. Pew

Internet & American Life Project report. Retrieved June 29, 2012 from http://

www.pewinternet.org/PPF/r/211/report_display.asp

Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of

trust in effective knowledge transfer. Management Science, 50(11), 1477–1490.

Levine, M., Cassidy, C., & Jentzsch, I. (2010). The implicit identity effect: Identity primes,

group size, and helping. British Journal Of Social Psychology, 49(4), 785-802.

doi:10.1348/014466609X480426

Levine, M. P., & Harrison, K. (2009). Effects of media on eating disorders and body

image. In Byrant, J. & Oliver, M. B. (Eds.), Media effects: Advances in theory and

research (pp. 490-516). New York: Lawrence Erlbaum Associates, Inc.

Levine M.P., Smolak, L., Hayden, H. (1994). The relation of sociocultural factors to eating

attitudes and behaviors among middle school girls. Journal of Early Adolescence, 14,

472–491.

Levinson, P. (2009). New new media. Boston: Allyn & Bacon.

Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., & Tomkins, A. (2005) Geographic

routing in social networks. Proceedings of National Academy of Sciences, 102(33) 11,

623-11,628.

Lin, C. A. (1998). Use of sex appeals in prime-time television commercials. Sex Roles, 38, 461–

475.

Livingstone, S. (2008). Taking risky opportunities in youthful content creation: teenagers' use of

social networking sites for intimacy, privacy and self-expression. New media & society, 145

10(3), 393-411.

Luthanen, R., & Crocker, J. (1992). A Collective Self-Esteem Scale: Self-Evaluation of One's

Social Identity. Personality and Social Psychology Bulletin, 18(3), 302-318.

Lorenzen, L.A., Greive, F. G., & Thomas, A. (2004). Exposure to muscular male

models decreases men’s body satisfaction. Sex Roles, 51, 743-748.

MacKenzie, D. A., & Wajcman, J. (1985). The Social Shaping of Technology. Maidenhead, UK:

Open University Press.

Martin, M.C., Kennedy, P.F. (1993). Advertising and social comparison: consequences for

female preadolescents and adolescents. Psychology Marketing, 10, 513–530.

Matheson, K., & Zanna, M. P. (1990). Computer-mediated communications: The focus is on

me. Social Science Computer Review, 8, 1–12 (Special issue: Computing: Social and

policy issues).

Mazur, A. (1986). U.S. trends in feminine beauty and overadaptation. The Journal of Sex

Research, 22(3), 281-303.

McCabe, M., & Ricciardelli, L. (2001). Parent, peer and media influences on body image

and strategies to both increase and decrease body size among adolescent boys and girls.

Adolescence, 36(142), 216–225.

McCreary, D. R., & Sadava, S. W. (1999). TV viewing and self-perceived health,

weight, and physical fitness: Evidence for the cultivation hypothesis. Journal of Applied

Social Psychology, 29, 2342-2361.

McKinney, B. C., Kelly, L., & Duran, R. L. (2012). Narcissism or Openness?: College Students’

Use of Facebook and Twitter. Communication Research Reports, 29(2), 108-118.

doi:10.1080/08824096.2012.666919 146

McLaughlin, C., & Vitak, J. (2012). Norm evolution and violation on Facebook. New Media &

Society, 14(2), 299-315. doi:10.1177/1461444811412712

McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in

social networks. Annual Review of Sociology, 27(1), 415–444.

Mertler, C. A., & Vannatta, R. A. (2010). Advanced and multivariate statistical methods:

Practical application and interpretation (4th ed). Glendale, CA: Pyrczak Publishing.

Mishkind, M. E., Rodin, J., Silberstein, L. R., & Striegel-Moore, R. H. (1986). The embodiment

of masculinity: Cultural, psychological. and behavioral dimensions. American Behavioral

Scientist, 29, 545-562.

Moral-Toranzo, F., Canto-Ortiz, J., & Gómez-Jacinto, L. (2007). Anonymity effects in

computer-mediated communication in the case of minority influence. Computers in

Human Behavior, 23(3), 1660-1674. doi:10.1016/j.chb.2005.09.002

Moscovici. S., & Zavalloni. M. (1969). The group as a polarizer of attitudes. Journal of

Personality and Social Psychology, 12, 125-135.

Murnen, S. K., Smolak, L., Mills, J. A., & Good, L. (2003). Thin, sexy women and

strong muscular men: Grade-school children’s responses objectified images of women

and men. Sex Roles, 49, 427-437.

Nakayama, T., Takahashi, Y., Shimbo, T., Uchida, C., Miyaki, K., & Sakai, M. (2009). Potential

Benefits and Harms of a Peer Support Social Network Service on the Internet for People

With Depressive Tendencies: Qualitative Content Analysis and Social Network Analysis.

Journal Of Medical Internet Research, 11(3), 4. doi:10.2196/jmir.1142

National Center for Education Statistics. (2012). Bowling Green State University—Main

Campus. Retrieved from: http://nces.ed.gov/. 147

National Geographic. (2011) Taboo: Beauty [Documentary]. United States: National Geographic

Channel.

Nemeroff, C. J., Stein, R. I., Diehl, N. S., & Smilack, K. M. (1994). From the

Cleavers to the Clintons: Role choices and body orientation as reflected in magazine

article content. International Journal of Eating Disorders, 16, 167–176.

Newcomb, T. M. (1943). Personality and Social Change: Attitude Formation in a

Student Community. New York: Dryden.

Newcomb, T. M., Koenig, K. E., Flacks, R., & Warwick, D. P. (1967). Persistence and

Change: Bennington College and Its Students after Twenty-five Years. New York: Wiley.

Nickson, C. (2009). The history of social networking. Retrieved from:

http://www.digitaltrends.com/features/the-history-of-social-networking/.

Ogden, J., & Mundray, K. (1996). The effect of the media on body satisfaction:

The role of gender and size. European Eating Disorders Review, 4, 171-181.

O’Sullivan, P. B. & Flanagin, A. (2003). Conceptualizing “flaming” and other

problematic communication. New Media & Society, 5(1), 69-94.

Ouwerkerk, J. W., De Gilder, D., & De Vries, N. K. (2000). When the going gets tough, the

tough get going: Social identification and individual effort in intergroup competition.

Personality and Social Psychology Bulletin, 26, 1550-1559.

Paicheler, G. (1977). Polarization of attitudes in homogeneous and heterogeneous groups.

European Journal of Social Psychology, 9, 85–96.meta-analytic review of social

influence processes. Psychological Bulletin, 115, 323–345.

Paolillo, J. C., & Wright, E. (2005). Social network analysis on the semantic web:

Techniques and challenges for visualizing FOAF. In V. Geroimenko & C. Chen (Eds.), 148

Visualizing the Semantic Web (pp. 229–242). Berlin: Springer.

Papacharissi, Z. (2002). The presentation of self in virtual life: Characteristics

of personal home pages. Journalism & Mass Communication Quarterly, 79(3), 643-660.

Papacharissi, Z. (2009). The virtual geographies of social networks: a comparative analysis of

Facebook, LinkedIn and ASmallWorld. New Media & Society, 11(1/2), 199-220.

Papacharissi, Z. (2011). (Ed.). A networked self: Identity, community, and culture on

social network sites. New York: Routledge.

Park, S. (2005). The influence of presumed media influence on women’s desire to be thin.

Communication Research, 32, 594-614.

Parks, M. R., & Floyd, K. (1996). Making friends in cyberspace. Journal of Communication, 46

(1), 80-97.

Pompper, D., Soto, J., & Piel, L. (2007). Male body image and magazine standards:

Considering dimensions of age and ethnicity. Journalism & Mass Communication

Quarterly, 84(3), 525-545.

Pope, H. G., Olivardia, R., Gruber, A., & Borowiecki, J. (1999). Evolving ideals of

male body image as seen through action toys. International Journal of Eating Disorders,

26, 65-72.

Pope, H. G., Phillips, K., & Olivardia, R. (2000). The Adonis complex. New York: Free Press.

Posavac, H. D., Posavac, S. S., & Posavac, E. J. (1998). Exposure to Media Images of Female

Attractiveness and Concern with Body Weight Among Young Women. Sex Roles,

38(3/4), 187-201. Retrieved from EBSCOhost.

Postmes, T., Spears, R., & Lea, M. (1998). Breaching or building social boundaries? SIDE-

effects of computer-mediated communication. Communication Research, 25, 689-715. 149

Postmes, T., Spears, R., & Lea, M. (1999). Social identity, group norms, and "deindividuation":

Lessons from computer-mediated communication for social influence in the group. In N.

Ellemers, R. Spears, & B. Doosje (Eds.), Social identity: Context, commitment, content.

Oxford: Blackwell.

Postmes T, Spears R, & Lea M. (2002). Breaching or Building Social Boundaries? Side-Effects

of Computer-Mediated Communication. In R. L. Campbell (Ed.), Legal Issues in

Electronic Commerce. Concord, Ontario CA: Captus Press.

Postmes, T., & Spears, R. (1998). Deindividuation and anti-normative behavior: A meta-

analysis. Psychological Bulletin, 123, 238-259.

Postmes, T., & Spears, R. (2002). Contextual moderators of gender differences and stereotyping

in computer-mediated group discussions. Personality and Social Psychology Bulletin, 28,

1073-1083.

Postmes, T., Spears, R., & Lea, M. (2002). Intergroup differentiation in computer-

mediated communication: effects of depersonalization. Group Dynamics: Theory,

Research, and Practice, 6, 3–16

Postmes, T., Spears, R., Sakhel, K., &deGroot, D. (2001). Social influence in computer-

mediated communication: The effects of anonymity on group behavior. Personality and

Social Psychology Bulletin, 27, 1243–1254.

Prentice-Dunn, S., & Rogers, R. W. (1982). Effects of public and private self-

awareness on Deindividuation and aggression. Journal of Personality and Social

Psychology, 43, 503–513.

Prentice-Dunn, S., & Rogers, R. W. (1989). Deindividuation and the self-regulation of

behavior. In P. B. Paulus (Ed.), The psychology of group influence (2nd ed., pp. 86-109). 150

Hillsdale, NJ: Lawrence Erlbaum.

Putnam, (2000). Bowling alone: The collapse and revival of American Community. New

York: Simon & Schuster.

Reicher, S. D. (1984). Social influence in the crowd: Attitudinal and behavioral effects

of deindividuation in conditions of high and low group salience. British Journal of Social

Psychology,23, 341-350.

Reicher, S. D. (1987). Crowd behaviour as social action. In J. C. Turner, M. A. Hogg, P.

J. Oakes, S. D. Reicher, & M. S. Wetherell (Eds.), Rediscovering the social group: A self-

categorization theory (pp. 171-202). Oxford, England: Basil Blackwell.

Reicher, S. S., & Levine, M. M. (1994). On the consequences of deindividuation

manipulations for the strategic communication of self: identifiability and the presentation

of social identity. European Journal Of Social Psychology, 24(4), 511-524.

Reicher, S., Spears, R., & Postmes, T. (1995). A social identity model of

deindividuation phenomena. In W. Stroebe & M. Hewstone (Eds.), European Review of

Social Psychology (Vol. 6, pp. 161-198). Chichester, England: Wiley.

Reid, S. A., Cropley, C., & Hogg, M. A. (2005). A self-categorization explanation of

pluralistic ignorance. Top three paper, presented at the International Communication

Association, New York.

Resnick, P. (2001). Beyond bowling together: Sociotechnical capital. In J. Carroll

(Ed.), HCI in the New Millennium (pp. 247-272). Boston, MA: Addison-Wesley.

Richins, M.L. (1991). Social comparison and the idealized images of advertising. Journal of

Consumer Research, 18,71-83.

Rycyna, C., Champion, C., & Kelly A. (2009). First impressions after various types of deception: 151

Less favorable following expectancy violation. Basic and Applied Social Psychology

31(1): 40–48.

Scharrer, E. (2004). Virtual violence: Gender and aggression in video game advertisements. Mass

Communication and Society, 7(4), 393-412.

Schienker, B. R., Dlugolecki, D. W., & Doherty, K. (1994). The impact of self-presentations on

self-appraisals and behavior: The power of public commitment. Personality and Social

Psychology Bulletin, 20, 20-33.

Schooler, D., & Ward, L. M. (2006). Average Joe’s: Men’s relationships with

media, real bodies, and sexuality. Psychology of Men and Masculinity, 7(1), 27-41.

Sherif, M. (1956). Experiments in group conflict. Scientific American, 195, 54-58.

Shim, M., Lee, M. J., & Park, S. H. (2008). Photograph use on social network sites among

South Korean college students: The role of public and private self-consciousness.

CyberPsychology & Behavior, 11(4), 489-493.

Silverstein, B., Perdue, L., Peterson, B., & Kelly, E. (1986). The role of the mass media

in promoting a thin standard of bodily attractiveness for women. Sex roles, 14, 519-532.

Silverstein, B., Peterson, B., & Perdue, L. (1986a). Some correlates of the thin standard of bodily

attractiveness for women. International Journal of Eating Disorders, 5(5), 895-905.

Singleton, R. A., & Straits, B. C. (2005). Approaches to social research. New York: Oxford

University Press.

Smolak, L. (1996). National Eating Disorders Association/Next Door Neighbors Puppet

Guide Book. Seattle, WA: National Eating Disorders Association.

Smolak, L., Murnen, S. K., & Thompson, J. K. (2005). Sociocultural influences

and muscle building in adolescent boys. Psychology of Men & Masculinity, 6, 227-239. 152

Spears, R., Doosje, B., & Ellemers, N. (1997). Self-stereotyping in the face of threats to group

status and distinctiveness: The role of group identification. Personality and Social

Psychology Bulletin, 23(5), 538-553.

Spears, R. R., Lea, M. M., Corneliessen, R. A., Postmes, T. T., & Ter Haar, W. W. (2002).

Computer-Mediated Communication as a Channel for Social Resistance: The Strategic

Side of SIDE. Small Group Research, 33(5), 555. Retrieved from EBSCOhost.

Spears, R., Lea, M., & Lee, S. (1990). De-individuation and group polarization in

computer-mediated communication. British Journal of Social Psychology, 29, 121-134.

Spears, R., Postmes, T., Lea, M., & Wolbert, A. (2002). When Are Net Effects Gross Products?

Communication. Journal Of Social Issues, 58(1), 91.

Spitzer, B. L., Henderson, K. A., & Zivian, M. T. (1999). Gender differences in

population versus media body sizes: A comparison over four decades. Sex Roles, 40,

545–565.

Stanford, J., & McCabe, M. (2005). Sociocultural influences on adolescent boys’

body image and body change strategies. Body Image, 2, 105-113.

Stefanone, M., Lackaff, D., & Rosen, D. (2011). Contingencies of self-worth and social-

networking-site behavior. Cyberpsychology, Behavior, and Social Networking,

14, 41-49. doi: 10.1089/cyber.2010.0049

Stice, E., Schupak-Neuberg, E., Shaw, H.E., &Stein, R.I. (1994). Relation of media exposure to

eating disorder symptomatology: An examination of mediating mechanisms. Journal of

Abnormal Psychology,103, 836-840.

Stice, E., & Shaw, H. (1994). Adverse effects of the media portrayed thin-ideal on women, and

linkages to bulimic symptomatology. Journal of Social and Clinical Psychology,13, 288- 153

308.

Stice, E., Spangler, D., & Agras, W. S. (2001). Exposure to media-portrayed thin-ideal images

adversely affects vulnerable girls: A longitudinal experiment. Journal of Social and

Clinical Psychology, 20(3), 270-288.

Strano, M. (2001). As time goes by: Ritualized remembering through wedding photography.

Unpublished doctoral dissertation, University of Pennsylvania, Philadelphia, PA.

Strano, M. (2008). User descriptions and interpretations of self-presentation through

Facebook profile images. Cyberpsychology: Journal of Psychosocial Research on

Cyberspace, 2(2).

Stross, R. (April, 2012). Social networks, smaller and smaller. The New York Times.

Retrieved from http:/www.nytimes.com.

Stutzman, F. (2006). An evaluation of identity-sharing behavior in social network communities.

Journal of the International Digital Media and Arts Association, 3(1), 10–18.

Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G.

Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47).

Monterey, CA: Brooks/Cole.

Tanis, M., & Postmes, T. (2003). Social cues and impression formation in CMC. Journal of

Communication, 53, 676 – 693.

Terry, D. J., & Hogg, M. A. (2001). Attitudes, behaviour, and social context: The role of

norms and group membership in social influence processes. In J. P. Forgas & K. D.

Williams (Eds.), Social influence: Direct and indirect processes (pp. 253-270). New

York: Psychology Press.

Thompson, J.K., Heinberg, L.J., Altabe, M., & Tantleff-Dunn, S. (1999). Exacting beauty: 154

theory, assessment, and treatment of body image disturbance. Washington, DC:

American Psychological Association.

Thornton, B., & Maurice, J. (1997). Physique contrast effect: Adverse impact of idealized body

images for women. Sex Roles, 37, 433-439.

Toma, C. L., & Hancock, J. T. (2010). Looks and lies: The role of physical attractiveness

in online dating self-presentation and deception. Communication Research 37(3): 335–

351.

Tong, S., Van Der Heide, B., Langwell, L., & Walther, J. (2008). Too much of a good

thing? The relationship between number of friends and interpersonal impressions

on Facebook. Journal of Computer-Mediated Communication, 13, 531-549. doi:

10.1111/j.1083-6101.2008.00409.x

Tufekci, Z. (2008). Grooming, gossip, Facebook and Myspace. Information,

Communication & Society, 11(4), 544-564. doi:10.1080/13691180801999050

Turner, J. C. (1991) Social influence. Pacific Grove, CA: Brooks/Cole.

Turner, J. C. (1999). Some current issues in research on social identity and self-categorization

theories. In. N. Ellemers, R. Spears & B. Doojse (Eds), Social Identity: Context,

Commitment, Content. Oxford: Blackwell.

Turner, J. C., M. A. Hogg, J. Oakes, S. D. Reicher & R. L. Webb (1987). Re-Discovering the

Social Group: A Self-Categorization Theory. Oxford: Blackwell.

Turner, J. C., & Oaks, P. J. (1986). The significance of the social identity concept for

social psychology with reference to individualism, interactionism and social influence.

British Journal of Social Psychology, 25, 237-252.

Turner, J. C. , & Oakes, P. J. (1989). Self-categorization theory and social influence. 155

In P. B. Paulus (Ed.), The psychology of group influence (2nd ed., pp. 233-275. Hillsdale,

NJ: Lawrence Erlbaum.

Undefined. (April, 2008). Radio One acquires BlackPlanet. Retrieved June, 27, 2012, from:

http://www.BlackPlanet.com.

Undefined. (2011). Facebook statistics. Retrieved August 10, 2011, from

http://www.facebook.com/press/info.php?statistics.

Undefined. (2012). Pinterest Statistics. Retrieved June, 20, 2012, from http://wwwpinterest.com

Undefined. (2012). Twitter Growth Statistics. Retrieved June, 15, 2012 from

http://www.Twitter.com.

Valkenburg, P., Peter, J., & Schouten, A. P. (2006). Friend networking sites and their

relationship to adolescents’ well-being and social self-esteem. CyberPsychology &

Behavior, 9(5): 584-590. doi:10.1089/cpb.2006.9.584.

Valkenburg, P.M., Schouten, A.P., & Peter, J. (2005). Adolescents’ identity experiments on

the Internet. New Media & Society, 7(3), 383–402.

Waechter, N., Subrahmanyam, K., Reich, S. M., & Espinoza, G. (2010). Youth Connecting

Online: From Chat Rooms to Social Networking Sites. At The Interface / Probing The

Boundaries, 69 151-178.

Walther, J. B. (1994). Anticipated ongoing interaction versus channel effects on

relational communication in computer mediated interaction. Human Communication

Research, 20, 473–501.

Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal,

and hyperpersonal interaction. Communication Research, 23, 3-43

Walther, J. B. (2007). “Selective self-presentation in computer-mediated communication: 156

Hyperpersonal dimensions of technology, language, and cognition.” Computers in

Human Behavior.

Walther, J. B. (2009). Theories, boundaries, and all of the above. Journal of Computer-Mediated

Communication, 14, 748-752.

Walther, J., Anderson, J. F., & Park, D. W. (1994). Interpersonal effects in computer-

mediated interaction: A meta-analysis of social and anti-social communication.

Communication Research, 21, 460–487.

Walther, J. B., Van Der Heide, B., Kim, S., Westerman, D., Tong, S. T. (2008). The role of friends’

behavior on evaluations of individuals’ Facebook profiles: Are we known by the company we keep?

Human Communication Research, 34, 28-49

Walther, J. B., Carr, C. T., Choi, S. S., DeAndrea, D. C., Kim, J., Tom Tong, S., Van Der Heide,

B. (2011). Interaction of interpersonal, peer, and media influence sources online: A

research agenda for technology convergence. In Z. Papacharissi (Ed.), A networked self:

Identity, community, and culture on social network sites. (pp. 17-38). New York:

Routledge.

Walther, J. B., DeAndrea, D., Kim, J., & Anthony, J. C. (2010). The Influence of Online

Comments on Perceptions of Anti-marijuana Public Service Announcements on

YouTube. Human Communication Research, 36(4), 469-492. doi:10.1111/j.1468-

2958.2010.01384.x

Walther, J. B., & Parks, (2002). Cues filtered out, cues filtered in: Computer-mediated

communication and relationships. In M. L. Knapp & J. A. Daly (eds.), Handbook of

interpersonal communication (3rd ed., pp. 529-563). Thousand Oaks, CA: Sage.

Wang, Z., Walther, J. B., & Hancock, J. T. (2009). Social identification and 157

interpersonal communication in computer-mediated communication: What you do versus

who you are in virtual groups. Human Communication Research, 35, 358-368.

Wann, D., & Branscombe, N. (1990). Die-hard and fair weather fans: Effects of

identification on BIRGing and CORFing tendencies. Journal of Sports and Social Issues,

14, 103-117.

Watermeyer, (2010). Social network science: Pedagogy, dialogue, deliberation.

Journal of Science Communication, 9(1), 1824-2049.

Wellman, B., Haase, A. Q., Witte, J., & Hampton, K. (2001). Does the internet increase,

decrease, or supplement social capital? Social networks, participation, and community

commitment. American Behavioral Scientist, 45(3), 436-455.

Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C.(1996).

Computer networks as social networks: Virtual community, computer-supported

cooperative work and telework. Annual Review of Sociology, 22, 213-238.

Williams, D. (2006). On and off the 'net: Scales for social capital in an online era.

Journal of Computer-Mediated Communication, 11 (2), article 11.

Wiseman, C. V., Gray, J. J., Mosimann, J. E., & Ahrens, A. H. (1992) . Cultural expectations of

women: An update. International Journal of Eating Disorders, 11, 85-89.

Yager, Z., & O'Dea, J. A. (2008). Prevention programs for body image and eating

disorders on University campuses: a review of large, controlled interventions. Health

Promotion International, 23(2), 173-189. doi:10.1093/heapro/dan004

Yurchisin, J., Watchravesringkan, K., & Brown McCabe, D. (2005). An exploration of identity-

recreation in the context of internet dating. Social Behavior and Personality, 33, 735-750.

Zhao, S., Grasmuck, S., & Martin, J. (2008). Identity construction on Facebook: Digital 158

empowerment in anchored relationships. Computers in Human Behavior, 24, 1816-1836.

Zimbardo, P. G. (1969). The human choice: Individuation, reason, and order versus

deindividuation, impulse, and chaos. In W. J. Arnold and D. Levine (Eds.), 1969

Nebraska Symposium on Motivation (pp. 237-307). Lincoln, NE: University of

Nebraska Press.

Zywica, J., & Danowski, J. (2008). The Faces of Facebookers: Investigating Social

Enhancement and Social Compensation Hypotheses; Predicting Facebook™ and Offline

Popularity from Sociability and Self-Esteem, and Mapping the Meanings of Popularity

with Semantic Networks. Journal of Computer-Mediated Communication, 14(1), 1-34.

159

APPENDIX A

Research Design

Pro-ideal Comments Body Ideal Pictures Anti-ideal Comments High Group Pretest Post-Test Identification Pro-ideal Comments No Body Pictures Anti-ideal Comments

Pro-ideal Comments Body Ideal Pictures Anti-ideal Comments Low Group Pretest Post-Test Identification Pro-ideal Comments No Body Pictures Anti-ideal Comments

160

APPENDIX A1

Hypothesized Directions of Influence

161

APPENDIX B

Stimulus Code

Stimulus Shell

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