Journalists on Twitter: Followers, Gender and Perceptions of Credibility

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A thesis presented to

the faculty of

the Scripps College of Communication of Ohio University and the Institute for Communication and Media Studies of Leipzig University

In partial fulfillment

of the requirements for the degrees

Master of Science in Journalism (Ohio University),

Master of Arts in Global Mass Communication (Leipzig University)

Briana D. Ekanem

May 2020

1 This thesis titled

Journalists on Twitter: Followers, Gender and Perceptions of Credibility

BRIANA D. EKANEM

has been approved for

the E.W. Scripps School of Journalism,

the Scripps College of Communication,

and the Institute for Communication and Media Studies by

Dr. Parul Jain

Associate Professor, Scripps College of Communication, Ohio University

Scott Titsworth

Dean, Scripps College of Communication, Ohio University

Christian Pieter Hoffman

Director, Institute for Communication and Media Studies, Leipzig University

2 Abstract

EKANEM, D. BRIANA, M.S., Journalism; M.A., Global Mass Communication,

May 2020

3749466

Journalists on Twitter: Followers, Gender & Perceptions of Credibility

Director of Thesis: Dr. Parul Jain

Committee Members: Dr. Hans Meyer, Rosanna Planner

As news consumers move online, journalists and media professionals face new challenges regarding the way in which they maintain trust with audiences. The spread of disinformation and misleading messages has become a pertinent issue for both consumers and creators on social media platforms, making source credibility online an incredibly important evaluation for consumers to consider and for journalists to adhere to. This study examined the perceptions of credibility for journalists on Twitter based on their gender and the number of followers they had. A total of 200 respondents with Twitter accounts residing in the U.S. took part in the study and a 2 (gender: male or female) x 2

(Twitter followers: high or low) between subjects experimental design with random assignment to view one of four conditions was utilized. Results of the study suggest that gender and the number of Twitter followers a journalist has does not influence a respondent’s evaluation of their credibility, but the number of followers a female journalist has on Twitter has a marginal effect on her perceived trustworthiness, a dimension of credibility, while the same is not true for a male journalist.

Dedication

This thesis is dedicated to my friends, both in Ohio and in Leipzig, and my family, whose

love, support and encouragement inspired me to complete this research.

None of this would have been possible if it weren’t for my incredible parents,

Kristin and Roy.

Acknowledgments

Thank you to my thesis committee, Dr. Parul Jain (Ohio University), Dr. Hans

Meyer (Ohio University) and Rosanna Planner (Leipzig University), for their professional and expert guidance throughout the research process. I am incredibly thankful for my time at Ohio University and the E.W. Scripps School of Journalism and I am especially thankful for the opportunity to participate in the double-degree program which has certainly changed my life for the better.

Table of Contents

Page

Abstract ...... 3 Dedication ...... 4 Acknowledgments ...... 5 List of Tables ...... 7 List of Figures ...... 8 Chapter 1: Introduction ...... 9 Chapter 2: Literature Review...... 14 The Media and its Audience ...... 14 Credibility ...... 15 Gender ...... 19 Twitter ...... 21 Chapter 3: Hypotheses ...... 26 Chapter 4: Methodology ...... 27 Manipulations ...... 28 Measures ...... 32 Credibility ...... 32 Social Attraction ...... 33 News Affinity...... 33 Twitter Frequency...... 34 News Media Literacy...... 35 News Motives...... 36 News Skepticism ...... 36 Demographic Variables...... 37 Chapter 5: Analyzing Results ...... 39 Chapter 6: Discussion ...... 50 Limitations and Future Research ...... 55 References...... 59 Appendix A ...... 69 Appendix B ...... 72

List of Tables

Page

Table 1 Pearsons Correlation Among Variables ...... 40 Table 2 Descriptive Statistics for Percieved Credibility Score ...... 41 Table 3 ANCOVA Summary for Percieved Credibility Score ...... 42 Table 4 Descriptive Statistics for Percieved Competence Score ...... 43 Table 5 ANCOVA Summary for Percieved Competence Score ...... 44 Table 6 Descriptive Statistics for Percieved Trustworthiness Score ...... 45 Table 7 ANCOVA Summary for Percieved Trustworthiness Score ...... 46 Table 8 Descriptive Statistics for Percieved Social Attraction ...... 48 Table 9 ANOVA Summary for Percieved Social Attraction ...... 48

List of Figures

Page

Figure 1. Manipulation 1 – Male journalist with high follower count...... 30 Figure 2. Manipulation 2 – Female journalist with high follower count...... 30 Figure 3. Manipulation 3 – Male journalist with low follower count...... 31 Figure 4. Manipulation 4 – Female journalist with low follower count...... 31 Figure 5. ANCOVA Interaction – Trustworthiness...... 47 Figure 6. ANOVA Interaction – Social Attraction...... 49

Chapter 1: Introduction

Journalism and journalists themselves have an important and unique role in the digital world we live in. With expectations for faster and more dynamic news today than ever before, it is only natural that journalists have added social media to their reporting toolkit.

From real-time reporting to headhunting sources, social media has been an important feature of the world of a modern journalist, one that entry-level candidates are expected to both understand and master. Because of this relatively recent addition to the practice of reporting and disseminating news via social media, it is important for practitioners and media scholars alike to better understand the ways in which social media affects audience perceptions of news messages and, specifically, the people that are crafting them.

Since 2016, trust in the media in the U.S. has been a focus of debate and discussion, particularly within the world of politics. According to a Knight Foundation (2019) survey on trust, media, and democracy, Americans “increasingly perceive the media as biased and struggle to identify objective news sources” (p. 4). While many believe that it has become harder to find objectivity in the news, almost two-thirds of American adults are using social media as a news source (Shearer & Matsa, 2019) and in 2018, the second most popular reason for internet users to use social media was to “stay up to date with news and current events” (Statista, 2018, p. 9). Along with the search for objectivity, social media users are subjected to more sources of information than ever before, creating an easy mechanism for disinformation to spread quickly on those platforms. This culture of fast-paced sharing has caused many users to both knowingly and unwittingly share

inaccurate or misleading content and journalists are not excluded from those who are susceptible (Wardle, 2017). Social media offers several practical uses for journalists, but in an online environment that has been criticized for being at the center of the spread of false and biased messaging, audiences are increasingly weary of trusting the messages they read, and the sources of the information being shared (Shearer & Matsa, 2019).

Credibility online is a topic that is incredibly relevant today in an era where fast- paced news and online sharing platforms are merging and audience expectations for truth and accuracy remain. Social media is a relatively new medium for news that is continuously changing, requiring continuous research to better understand its effects.

Twitter, a leading social media site, is one platform that has grown since its inception and has quickly become an important outlet for both media professionals and their audiences as well as world leaders.

There are several aspects of Twitter use that may relate to perceptions of credibility such as the number of followers a user has, how many people he or she follows, the number of retweets and likes a particular tweet from that person gets, the content that person retweets and likes, and the way that person interacts with others on the platform

(Westerman, Spence & Van Der Heide, 2012). Like many other social media platforms,

Twitter allows users to personalize their profiles with biographies and photos. Many users take the opportunity to identify themselves based on things like their interests or occupation. With all of these profile elements that already exist on Twitter, this research

study aims to examine how certain features unique to twitter may influence credibility perceptions.

In 2018, it was reported that new editorial staff members at the Athletic, a sports media outlet, were told they could earn bonuses for converted followers on social media, specifically one dollar per new subscriber converted from a journalist’s established followers (Gordon, 2018). Also in 2018, more than a dozen members of the media were found to have paid for followers, specifically on Twitter (Confessore et al., 2018).

Conversations surrounding ethical guidelines for journalists arose following each of these incidents and several concerns regarding social media were put forth. Social media platforms have seen a plethora of fake accounts on their services, and there are several companies that sell followers, commonly known as “bots” that generate content on a regular basis and are not typically connected to any one, real individual. Many have criticized people accused of taking part in this transaction, primarily those that work in professions where transparency is a valued component of their role in society, such as journalists. With growing concerns for the conduct of journalists on social media, outlets are trying to manage expectations for social media use in the newsroom while also making sure to encourage their reporters to avoid breaching journalism ethics while online (Rainey, 2018).

An added area of concern for the professional expectations for journalists online today is the differences between men and women within the industry. Women are a minority in leadership positions within the media industry, and despite an increase in

employment of women in many other industries in the United States since the 1970s, journalism has not seen the same surge in hiring women (York, 2017). In 2017, the

#MeToo movement went viral on social media and sparked awareness about sexual assault and harassment, primarily, against women. The movement, started by and largely focused on women, was also widely covered in the news and dominated headlines, but men penned more stories regarding the movement then women (Women’s Media Center,

2019). The movement saw many female journalists share their own stories of how they have been harassed online with targeted attacks on their gender or sexuality specifically, and because they are expected to do a lot of their job online, many women said they felt that they, “had no choice but to face the harassment” (Women’s Media Center, 2019, p.

43). The way women, and specifically female journalists, are treated online can have consequences on their mental and physical health which in turn can affect their ability to work and might have financial implications. For these reasons, it is important to understand and consider the ways in which audiences perceive women online and whether or not gender plays a role in that evaluation in order to encourage changes in the industry and in society.

Measuring how news consumers perceive journalists online is an important topic to consider in order to understand how existing cues on social media may affect those perceptions. As a result of these new online dynamics, previous credibility measures that have been used in research relating to traditional media may not be sufficient in measuring source credibility on social media. In order to better understand source

credibility online, it is important to consider existing cues such as number of followers and perceived gender in order to contribute an understanding for source credibility on social media. Based on recent events in the industry and current expectations for news online, this study will seek to answer the following research question; How do existing cues, such as number of followers on social media platforms and perceived gender, effect credibility perceptions of journalists?

Chapter 2: Literature Review

The Media and its Audience

The way people interact with all forms of media has changed throughout history and continues to evolve today. In 2018, newspaper circulation fell to the lowest level in the U.S. since 1940 (Barthel, 2019). While print newspapers were formally the most popular medium for news, they no longer are today due to the presence of digital subscription options. In 2018, the Internet was the most popular news platform among consumers in the U.S. overall at 60%, and while people aged 55 and older are turning to television most often, younger generations have moved online and to social media to get their news (Statista, 2018). The shift in medium preference has also changed the way media outlets operate and manage employees in order to best serve their audiences.

According to a Pew Research Center analysis of Bureau of Labor Statistics Occupational

Employment Statistics survey data (Grieco, 2019), newsroom employment across the country has declined since 2008, despite there being growth for digital-native news outlets, or outlets that started online. The structure of the media industry is likely to continue to adapt to the needs and wants of consumers. It is important to understand how audiences interact with media in order to be able to anticipate the direction the industry may be headed in the future and to better understand the larger impact the media may have on society.

Consumers differ in the way they understand and evaluate messages and with a shift to online news consumption, individuals are subjected to more information from

more sources than ever before. Because of this easy access to information, there are also a plethora of content creators online, making it tougher for consumers to know exactly what outlet messages are coming from and knowing whether or not those sources are reputable, making credibility, an important aspect for media members to prioritize.

Credibility

The conceptualization of credibility can be traced all the way back to Aristotle and his understanding of ethos as a combination of intelligence, character, and goodwill and the study of persuasion (McCroskey & Teven, 1999). While several researchers have incorporated other factors to describe the makeup of credibility including elements like safety, qualification and dynamism, (Hovland, Janis, & Kelly, 1953; Berlo, Lemert, and

Mertz, 1969), a consistent recognition of the importance of both trustworthiness and expertise has been established throughout (Hovland, Janis & Kelly, 1953). These elements, then, have become two pillars for understanding source credibility.

Trustworthiness has been defined using elements such as the source’s honesty and objectivity and focuses primarily on whether or not a source is perceived to be valid in their arguments, statements or claims (Epega, 2008). In many fields, and particularly in journalism, it is important that consumers view sources as trustworthy in order to effectively deliver messages and maintain readership, viewership or listeners.

Expertise has been defined as the extent to which message receivers believe the source to be knowledgeable (Epega, 2008) or to know the truth (Hu, 2015) and it has also

been made up of and defined as elements such as competence, intelligence, qualification and authoritativeness in previous research (McCroskey & Teven, 1999).

Whether or not a person is perceived as being attractive has also been considered alongside credibility evaluations. Interpersonal attraction, then, has been incorporated in previous credibility research, which includes dimensions relating to physical attraction, task attraction and social attraction and the concept has contributed several important themes surrounding the way people perceive others based on attractiveness and influence.

McCroskey and McCain (1974) developed an instrument for interpersonal attraction which has been a reliable measure for all three dimensions of attraction (physical, task and social). After analyzing previous interpersonal attraction research, the two found that the more people are attracted to one another, the more they will communicate with each other and the more we are attracted to a person, the more influence they have on us

(McCroskey & McCain, 1974).

There has been a substantial amount of credibility-focused research over the years and there have been valuable insights regarding perceptions of credibility within the field of journalism. One study, using a sample of Swiss, German and Austrian television markets, considered gender and age and their effect on audience perceptions of newscasters’ credibility. They found that when news was read on air by women, they were perceived as being more credible, but their male collogues were found to be considered as more credible people overall, while older male newscasters were deemed the most credible (Weibel, Wissmath & Groner, 2008).

Existing research that is focused on source credibility have used the message as the key component of the credibility measurement, sometimes making results unclear about whether respondents are evaluating the source themselves or the message they have shared (Weibel, Wissmath & Groner, 2008). Because of this, it is important to understand factors influencing source credibility and subsequent implications. Brann and Hime

(2010) found that despite technological advancements since Weibel, Wissmath and

Groner’s (2008) research, men continued to be deemed as more credible than women in the newscaster position. In her research, Mohammed (2012) focused on the effects of spokesperson ethnicity and gender on credibility related to public relations practitioners.

The results of her study found that the ethnicity and gender of a PR spokesperson did not have a statistically significant effect on audience perceptions of credibility, but she did note that male spokespersons were rated as being more altruistic than their female counterparts, (Mohammed, 2012).

Social media has become an added platform for journalists and media outlets to share news and it differs greatly from the stylistic and distribution norms of television, newspaper and radio. It is worth considering then, that audiences might assess credibility differently when evaluating sources on social media and several studies have helped to establish important insights regarding source credibility online and on social media.

Using a website service called Klout, a system-generated tool that helps in measuring influence based on any particular user’s potential to “drive action” on social media (Edwards, et. al, 2013), one research team found that an indicator of a person’s

influence included on his or her Twitter profile significantly affects perceptions of credibility. Their results illustrated that a high Klout score led to perceptions of high competence and character while low Klout scores featured on the same profiles received lower competence and character scores (Edwards et al., 2013). With a similar goal of studying credibility online, Hu (2015) conducted a social media focused credibility research study from a marketing perspective. Hu (2015) used “hubs,” or popular figures on social media, to survey audience perceptions of their credibility and found that there is a positive relationship between Internet and Twitter involvement and perceptions of source credibility on social media where the higher a respondent indicated they valued the Internet or Twitter and the more often they indicated they used the service or the platform, the higher they perceived a sources credibility (Hu, 2015).

There have also been relevant research findings regarding credibility and images.

Mohammed (2012) found that gender and ethnicity were not powerful enough variables on credibility scores but questioned whether or not the use of additional manipulations, like the spokesperson’s photograph, would have provided different findings. It is worth considering how images affect perceptions of credibility because previous research has found that people “are not able to ignore pictorial social cues any more than they can ignore the same cues in real life” (Reeves & Nass, 2006, p. 47). Previous research has also found that visual items such as username and user profile image are considered when making credibility evaluations (Han, 2018). Because pictures offer the opportunity to

perceive additional characteristics about message senders, they can also “focus attention on primitive social cues” such as gender (Reeves & Nass, 2006, p. 48).

Gender

In 2018, women accounted for 46.9 % of the U.S. workforce and 51.7% of news analysts, reporters and correspondents were women (Bureau of Labor Statistics, U.S.

Department of Labor, 2018). While women hold more of the positions in traditional journalists’ occupations, they also represent the majority of students in journalism programs (De Vuyst, 2017). Despite these ratios, news has traditionally been a male- dominated field (Baitinger, 2015) and women are still underrepresented in leadership roles within the news media workforce, typically holding positions that are “associated with low levels of power and prestige” (De Vuyst, 2017, p. 252). These positions tend to be related to “soft news” topics such as features, human-interest stories and small-town news while men dominate “high-status” news arenas such as politics and business

(Steiner, 2017). Not only is there a disproportional relationship between men and women within the newsroom, but women are more often subjected to harassment than men, especially online. Because more journalists are online than ever before, people that are critical of any particular journalists’ reporting can take to the comment section of an article or find journalists on social media to take up their complaints or concerns directly.

Some critics online continuously target journalists and make rude or outlandish statements on social media platforms that belong to them; these critics are often referred to as “trolls” (Adams, 2017). “Trolls” target women more than men, making especially

offensive remarks that in turn lead women to be cautious or less vocal online, prohibiting some women from speaking freely (Adams, 2017). As Adams (2017) writes, “Women who do not have freedom of speech cannot be themselves, progress their careers or play an equal part in society” (p. 856). Many women that are expected to use social media to complement their professions also subject themselves to harassment online which may add to an already difficult relationship between their personal lives and online persona and managing professional expectations.

Understanding the difference in expectations of male and female journalists can be helped by applying the Social Role Theory (SRT) in the field of journalism. The basis of the social role theory is focused on the “division of people into different roles based on group membership” creating “stereotypical expectations of a group based on the traits required to fulfill that role” (Schneider & Bos, 2019, p. 178). SRT also suggests that, from a young age, girls are taught to maintain “communal” traits and roles or to seek work that helps others while boys are taught to inhabit traits that are more “agentic” or power-related roles that often involve leadership and decision making (Schneider & Bos,

2019). These established expectations for men and women then apply to the ways in which societies view them in professional settings. In the world of sports reporting, for example, women face a unique set of challenges in earning respect and trust of sports audiences. Past research has found that audiences see women as “out of place” in the field of sports media and perceive men to be more credible than women newscasters, especially when on television and the gender of each journalist is easier to assume (Greer

& Jones, 2012). Greer and Jones (2012) discussed the impact of traditional gender roles on the ways audiences evaluated the job of sports analysts. They found that audiences preferred to see women cover traditionally feminine sports and men cover traditionally masculine sports, but women were found to be more competent overall. Their findings relate to a larger group of research related to differences in perceptions of knowledge and expertise based on gender. Gender stereotypes online are prevalent, and people have a tendency to simplify their evaluations of messages using gender stereotypes because there are pre-existing, established social cues for how to interact with each gender in real life (Reeves & Nass, 2006). Those gender stereotype cues, according to Reeves and Nass

(2006), activate “radically different ways of thinking about what is presented” (p. 167) while online.

Twitter

Since its launch in 2009, Twitter has quickly become an important platform in the social media landscape. The website now has 330 million active users worldwide as of

April 2019 and 49.5 million active users in the U.S. alone, the highest in one country

(Statista, 2018). Twitter allows users to craft and share messages (tweets) with their followers. Users can then re-post (retweet) messages they relate to or posts they would like to help get more visibility. If someone wants to show appreciation for a post but doesn’t necessarily want to re-post the message on his or her own profile (timeline), they can simply “like” the tweet by clicking on the heart shaped icon below it. The mechanics of Twitter are set up to make using the platform simple and convenient for both the

audience and the message sender while also providing signifiers for both the quantity

(e.g. followers) and quality (e.g. likes, retweets) of their interactions.

Twitter has been the starting line for several international social movements and campaigns that have helped to spread messages across the world at a fast pace (e.g. Arab

Spring, Occupy Wall Street). It has become a platform that also serves as a tool for journalists to promote their stories and interact with readers, viewers and sources all in one location. The site lends itself to on-the-go message sending and receiving with 80 percent of active users accessing the site through mobile devices or on the mobile application (Smith, 2019). Many world leaders and public figures use Twitter to share news related to their brands, companies or causes, making it a platform that journalists must understand in order to keep up with important news and events internationally.

While he is not the first U.S. president to use Twitter while in the White House, Donald

Trump often uses the microblogging service several times within a single day to react and comment on current events, requiring political journalists to interact with content on the site throughout the day as well. Twitter is not only an important tool for political journalists, but for all reporters hoping to keep up with trending topics and to be able to interact with his or her audience. There are several tools within the app that can aid organization and categorization for journalists on any particular beat such as Twitter’s

“lists,” which allow users to curate a personal list of his or her preferred sources on the platform for any topic. A study that looked at Washington DC-based journalists and the differences in the way male and female journalists used the platform found that men were

far more likely to tweet more, to retweet male colleagues, and had twice as many followers as their female counterparts, on average (Usher, Holcomb & Littman, 2018).

The study also found that male journalists almost exclusively amplified and engaged with other men in the field, while women also engaged with one another more often than with men on Twitter (Usher, Holcomb & Littman, 2018).

Twitter has been established as a necessary tool for journalists to use professionally, and it is likely that there will continue to be an expectation for journalists to have mastered the platform prior to securing a job in the years to come (Hu, 2015). In

2015, Twitter’s founder Jack Dorsey exclusively thanked journalists for helping to make

Twitter “what it is” posting a series of Tweets that read:

Journalists play a critical role in our society: keep the world honest and

balanced. They are true servants to the people….After tech early-adopters,

journalists were next to take to Twitter. They used it as a source, to break

news, and to link their work….Journalists were a big part of why we grew

so quickly and still a big reason why people use Twitter: news. It’s a

natural fit…Journalists make Twitter better by providing context, research,

and a balanced perspective drawn from what the people experience….

(Kaufman, 2015, p. 3).

An added element to any one person, brand or company’s Twitter profile is a badge of verification. Twitter has established a list of verified users and signified them with checkmark badges next to their profile name in an effort to inform people that the

account “of public interest is authentic” (Verified account FAQs, p. 1p.5). On its website,

Twitter explains verified users to typically be in “music, acting, fashion, government, politics, religion, journalism, media, sports, business, and any other key areas” (p. 5).

While the company has created a cue of authenticity to appear on qualified profiles, users must individually apply for verification and in 2017, the platform was largely criticized for verifying the account of the Charlottesville, “Unite the Right” rally organizer and white supremacist, Jason Kessler. The “coveted blue checkmark” (p. 3) has historically been hard to earn, and many questioned how Kessler was approved by the company despite there being guidelines in place (Perez, 2017). As a result, Twitter paused the verification process, but those who achieved verified status prior to the pause still sport the badge on their profiles. The company has yet to re-open applications with a note on the website stating, “…our verified account program is currently on hold. We are not accepting any new requests at this time” (Verified account FAQs, p. 1). Because Twitter has paused verifying new profiles, credible sources that have since joined the platform will have to wait to receive a badge until the applications re-open. While there are many authentic accounts that do not have verified profiles, the pause in the verification process has reignited discussions surrounding how audiences perceive accounts as being credible.

With rapid advancements in technology and an increased interest and importance in trusting sources online, it is incredibly important to take a closer look at the ways in which journalists use Twitter and how their actions and representation online can affect audiences’ perceptions of their credibility. Because more consumers of news are using

social media as their preferred source, journalists must maintain the trust of their readers as credible sources and take strides to understand how they can differentiate themselves from unauthentic authors and generators in this new and largely populated arena all in an effort to mitigate the spread of false and misleading information online.

Chapter 3: Hypotheses

This researcher set out to answer the following research question; how do existing cues, such as number of followers and perceived gender, effect credibility perceptions of journalists on social media? After conducting secondary research related to credibility, gender and online media, a basis for understanding common perceptions based on social media behavior and influence potential was established. Based on the research detailed in the previous chapter, this researcher proposes the following hypotheses to identify the effects of gender and number of followers on Twitter on journalists’ perceived credibility.

1. Hypotheses

H1: Participants will perceive a male journalist as more credible (based on

competence and trustworthiness) than a female journalist on Twitter.

H2: Participants will perceive a journalist with a high number of Twitter followers

as more credible (based on competence and trustworthiness) than a journalist with

a low number of Twitter followers.

H3: There will be an interaction effect between the gender of the journalist and

the number of followers he or she has on the participant’s perception of credibility

where participants will perceive a male journalist with a low number of Twitter

followers as more credible (based on competence and trustworthiness) than

participants who are shown the Twitter of a female journalist with a high number

of followers.

Chapter 4: Methodology

In order to answer the research question and test the subsequent hypotheses, a 2

(gender: male or female) x 2 (Twitter followers: high or low) between subjects experimental design with random assignment to view one of four conditions was utilized.

The experimental design is most suitable for answering the question the study sought to answer in a controlled setting ensuring high internal validity. Furthermore, random assignment of the subjects to experimental conditions helps reduce bias and provides more confidence in the findings. Participants were recruited using Amazon Mechanical

Turk (MTurk), an outsourcing marketplace that has been used in previous research and allows for a more diverse and representative sample than traditional samples using

American university students (Buhrmester, Kwang & Gosling, 2011). Parameters were set to establish each participant had a Twitter account and lived in the U.S and respondents were compensated $0.25 for completing the survey. Of 200 respondents who completed the survey, 82 were male and 118 were female and the participants primarily identified themselves as white (84%). MTurk also requires that “workers” are over the age of 18 which established that respondents to the survey were 18 and older.

After consenting to participate in the experiment and confirming the individual was not a robot with a captcha test, respondents were told they would see an image of a journalist’s Twitter profile and there would be several questions following the image.

Respondents could return to the image at any time. The profiles of the personas that were

shown were one of the four manipulations: a male journalist with a high Twitter follower count; a male journalist with a low Twitter follower count; a female journalist with a high

Twitter follower count; and a female journalist with a low Twitter follower count. After seeing one of the four manipulations of a journalist persona’s Twitter profile, participants then completed a survey relating to the personas they were shown, their media use, news affinity, news skepticism, thought processing and news literacy.

Manipulations

The images used for the journalists’ persona profiles were selected at random from the Chicago Face Database (CFD), the photos included were labeled as “HC” or

“happy, closed mouth” and were chosen for this experiment as they reflect those of professional headshots which are commonly used on Twitter. The photos selected were also each from the “white male” or “white female” categories to help in controlling for racial biases. Using the Social Security (2019) name data and U.S. Census Bureau (2016) surname data to find the most common first and last names in the U.S., the names for both journalists were assigned as “Mary Smith” and “John Brown” and their Twitter account handles were represented on each profile to match accordingly (i.e. @johnbrown,

@marysmith). The profile mockups were then created using a 2019 Twitter profile mockup template of the desktop version of the platform in Photoshop retrieved from

Creative Market. The profiles were made to be simple and excluded Tweets or long biographies in order to avoid credibility scores being attributed to messages rather than the personas on each profile. Each profile simply had “Journalist,” written in their

biography section to reinforce the persona’s profession. Figures 1-4 illustrate the manipulations.

Each persona was represented as following the same amount of people at 8,700

(8.7K). This number is the average number of people members of Twitter’s verified

(@verified) list of media accounts follow (Appendix A). Each persona was also set to have 27,000 (27K) Tweets, as that was the average number of Tweets members of

Twitter’s verified media list had sent as of September 2019. The follower counts were determined using Twitter’s verified media members list as well. First, the mean number of followers was calculated from the 94 members (after excluding outliers (>10M followers) and accounts belonging to outlets, not individuals or non-journalists) as being

716,938. The median follower count was 136,300 and was used to set the high and low follower counts for each journalist persona profile. To establish the “high” and “low” follower counts, three medians above and below the mean were used which resulted in the high follower count as 1,125,828 (1.1M) followers and the low follower count as

308,038 (308K). Participants were shown one of the four manipulations randomly (male journalist; high followers (1.1M), female journalist; high followers (1.1M), male journalist; low followers (308K), female journalist; low followers (308K). Please see figures 1-4 for the detailed manipulations.

Figure 1. Manipulation 1 – Male journalist with high follower count.

Figure 2. Manipulation 2 – Female journalist with high follower count.

Figure 3. Manipulation 3 – Male journalist with low follower count.

Figure 4. Manipulation 4 – Female journalist with low follower count.

Measures

Credibility. Perceived Credibility was measured after combining two scales for

Competence and Trustworthiness (McCroskey & Teven, 1999) to create a new variable for perceived Credibility Score (M =24.3; SD =9.72; α =.824). Both Competence and

Trustworthiness were measured on a 7-point scale, borrowed from McCroskey & Teven

(1999), where the reliabilities for each scale were greater than .80 (Competence α = .85,

Trustworthiness α = .92), Opposite characteristics were on either end of the scale, and each using six items. The six-items for Competence (M =4.91: SD =1.03: α =.93) included characteristics such as “Unintelligent; Intelligent” (M =4.94; SD =1.08). The six items for Trustworthiness (M =4.79; SD =1.10; α =.95 ) included characteristics such as

“Dishonest; Honest” (M = 4.75; SD =1.22). The credibility measurement was adapted for the purpose of this study from McCroskey and Teven’s (1999) 18-item scale which incorporated competence, trustworthiness and goodwill. The “caring” dimension of the scale, however, was excluded from the credibility measurement as it requires personal and in-person interaction with scale items such as, “Doesn’t care about me; Cares about me.” The competence and trustworthiness dimensions were chosen to create the credibility measurement as both have consistently been used throughout credibility research (Hovland, Janis, & Kelly, 1953; Berlo, Lemert, and Mertz, 1969; McCroskey and Teven, 1999; Westerman et al. 2012) yet still apply to measuring credibility in both traditional media research and online credibility research (Hu, 2015).

Social Attraction. Social Attraction was measured using a 7-point Social

Attraction (McCroskey & McCain, 1974) Scale (1, strongly disagree; 7, strongly agree) using five items (M =4.44; SD =1.13; α =.84 ). An example item is, “I think this journalist could be a friend of mine” (M =4.12; SD = 1.35). A dimension of interpersonal attraction, social attraction is intended to measure a social or personal liking property

(McCroskey & McCain, 1974). The other dimensions within the interpersonal attraction measurement, which has been used in previous credibility research, include physical attraction and task attraction items which ask questions related to the way an individual dresses (i.e. “The clothes he/she wears are not becoming.”) and the way an individual completes tasks (i.e. “He/she is a typical goof-off when assigned a job to do.”), both of which are not applicable to an experiment using online personas, such as this one

(McCroskey & McCain, 1974). Because measures relying on specific physical attributes and assignments require cues that do not exist in the context of social media, they cannot easily be applied to sources online (Hu, 2015). For these reasons, only the social attraction dimension was included as a control variable for the purpose of this study.

News Affinity. News Affinity refers to, “the degree to which one views access to news across media as an indispensable need” (Li, 2013). News Affinity was measured using a 7-point News Affinity (Li, 2013; Rubin, A, 1981) Scale (1, strongly disagree; 7, strongly agree) using five items (M =4.79; SD =1.67; α =.93) in order to create a News

Affinity score. The items included statements like the following; Reading news is one of the most important things I do every day (M =4.74; SD = 1.82). News affinity was an

important covariate to include in this study as it directly relates to an individual’s perception of the importance of news and how they view it as a component of their life

(Li, 2013). Because this study focused on perceptions of journalists online, whether or not a respondent values the news, as measured via the news affinity scale, may be a factor in influencing their perceptions toward the credibility of the source and therefore was included as a covariate in the analyses.

Twitter Frequency. Twitter Frequency, which determined how often respondents used Twitter, was measured on a 5-point scale (1, never; 5, many times a day) using three items (M =2.88; SD =.95; α =.76) in order to create a Twitter Frequency score. The three items included were: (1) How frequently do you use Twitter overall (M =3.46; SD

=1.14); (2) How frequently do you send Tweets about yourself (M =2.32; SD =1.12); and (3) How frequently do you share other’s Tweets (M =2.86, SD =1.20). Within the same section, participants were asked to indicate, using their closest guess, based on their memory, how many Twitter followers they have (M =1,938.34; SD =21,792.75) and how many accounts they follow on Twitter (M =324.86; SD =593.85). Twitter frequency was an important measurement to include in this study as previous research focused on credibility perceptions on Twitter found that there was a significant relationship between how often an individual indicated they used the platform and how they perceived a professional’s credibility (Hu, 2015).

News Media Literacy. News media literacy refers to, “the knowledge and motivations needed to identify and engage with journalism” (Maksl, Ashley & Craft,

2015, p. 29). News Media Literacy was made up of three scales based on Need for

Cognition, Media Locus of Control and News Media Knowledge Structures (Potter,

2012; Maksl, Ashley and Craft, 2015). The first scale, Need for Cognition (NFC), was based on a shortened version that was used in past research (Epstein et al. 1996; Maksl,

Ashley & Craft, 2015) with a 7-point scale (1, strongly disagree; 7, strongly agree) to measure the concept (α =.78) and included five items (M =4.95; SD =1.37; α =.85).

Need for cognition refers to “the degree to which one engages in mindful versus automatic thought-processing of news” (Maksl, Ashley & Craft, 2015, p. 33). One example of the items on the Need for Cognition Scale was: “I don’t like to have to a lot of thinking,” (M =.68; SD =.66). Next, the Media Locus of Control Scale (MLOC)

(Wallston, Wallston & DeVellis, 1978; Maksl, Ashley & Craft, 2015) was also an adapted scale from previous research (α =.64) in order to assess how participants evaluate their control over media influences by using a 7-point scale (1, strongly disagree; 7, strongly agree) and six items (M =4.77; SD =1.00; α =.77). One of the items included was: “If I am misinformed by the news media, it is my own behavior that determines how soon I will learn credible information” (M =4.96; SD =1.45). The last element incorporated in the News Media Literacy measurement was the News Media Knowledge

Structures (Potter, 2012; Maksl, Ashley & Craft, 2015) Score (M =10.20; SD =3.36), which included 15 multiple-choice questions where each question had only one correct

answer and the questions regarded news institutions, news production and possible effects of news consumption. Participants’ responses were scored to create a News Media

Knowledge Structure Score. One of the questions within the News Media Knowledge

Score was: “Most media outlets in the United States are…(a) For-profit business, (b)

Owned by the government, (c) Non-profit business, (d) Don’t know.”

News Motives. Similarly, News Motives was a measurement included in the questionnaire as motivations for news consumption relate to the relationship an individual respondent has with news media and may be a variable that could influence their evaluation of a journalists’ credibility. News Motives (M =4.70; SD =1.13, α =.61) was measured on a 7-point scale (1, strongly disagree; 7, strongly agree) adapted from prior research (Rubin, 1981; Maksl, Ashley & Craft, 2015) in order to determine how motivated respondents were to consume news using four items which included the following statements: “I don’t see what news does for me” (reverse coded) (M =5.67; SD

=1.59); “I follow the news because I’m supposed to” (M =2.69; SD =1.67); “I follow the news for my own good,” (M =5.10; SD =1.67); and “I follow the news because I like to”

(M =5.32; SD =1.75). A News Motives score was then created from this scale where a higher score indicated the participant was more intrinsically motivated for news consumption.

News Skepticism. News Skepticism was a measure incorporated in the questionnaire as it is relevant to credibility perceptions. As Yamamoto, Hmielowski,

Beam & Hutchens (2018) wrote, “a skeptical citizen actively engages with media by

analyzing political messages, questioning the motivation underlying the messages, evaluating the messages from a normative standpoint, and seeking information to validate or invalidate the messages to fully understand issues and make informed decisions” (p.

179). Borrowed from Maksl, Ashley and Craft (2015), News Skepticism (M =3.57; SD

=1.24; α =.88) was measured to determine the level of trust a respondent had in news media using a 7-point scale (1, strongly disagree; 7, strongly agree) and eight items, including the following statement: “I don’t think the news media can be trusted” (reverse coded).

Demographic Variables. Gender (dummy coded as 0 = male; 1 = female),

Political Affiliation and Race were also recorded. Political Affiliation (M =2.15 , SD

=.792) was coded as “Republican”= 1 (N =41) “Democrat” = 2 (N =96), “Independent”

= 3 (N =58), “Other” = 4 (N =3), and “Do not wish to answer” = 5 (N =2). If a respondent selected “Other,” they were asked to fill-in their political affiliation where

“Libertarian” was the only political affiliation added (N =2) and one respondent explained that they “…sometimes support democrats and sometimes independents but the main thing I look for is whether or not they are progressive.” Race was recorded as a

“select all that apply” response where “White” (N =168), “Black or African American”

(N =18), “American Indian or Alaska Native” (N =4),“Asian” (N =11),“Native Hawaiian or Pacific Islander” (N =0), “Latino/Hispanic” (N =7) were included as options with an

“Other” (N =1) field included as well for respondents who felt they were not represented

with the choices given. The one respondent that selected “Other” filled-in his or her race as “Hebrew Israelite.”

Chapter 5: Analyzing Results

In order to conduct an analysis of variance, it was important to establish that all required assumptions were met. To ensure the variables and covariates of interest were related to the dependent variable, correlations were run between the credibility score variable and each extraneous variable to establish significance (Table 1). The credibility perceptions were not significantly correlated with gender of the respondent, political affiliation, need for cognition, knowledge structure score, number of Twitter followers, number of Twitter accounts a respondent was following and media use score. Out of fourteen extraneous variables, six correlations were significant: Twitter Frequency (M

=2.88; SD =.95; r = .27; p < .01), News Skepticism (M =3.57; SD =1.24; r =.33; p < .01), News Motives (M =4.70; SD =1.13; r =.26; p < .01), Social Attraction

M =4.44; SD =1.13; r =.53; p < .01), News Affinity (M =4.79; SD =1.67; r =.26; p < .01), and Media Locus of Control (MLOC), (M =4.77; SD =1.00; r =.22; p < .01).

Table 1

Pearson Correlation matrix among variables M SD 1 2 3 4 5 6 7 8 9 1. Credibility 24.31 9.72

2. Twitter 2.88 .95 .27** Frequency

3. News 3.57 1.24 .33** .31** Skepticism

4. News Motives 4.70 1.13 .26** .30** .37**

5. News Affinity 4.79 1.67 .26** .30** .42** .69**

6. Social 4.44 1.13 .53** .20** .33** .38** .32** Attraction

7. Media Locus of 4.77 1.00 .22** .25** .33** .28** .28** .20** Control

8. Competence 4.90 1.03 .90** .20** .22** .24** .22** .46** .16**

9. Trustworthiness 4.79 1.10 .93** .26** .36** .25** .26** .50** .21** .70**

10. Gender .097 -.02 .05 -.08 .01 .05 -.07 .14* .07

** p <.01, *p <.05

To test H1, H2 and H3, a Two-way ANCOVA was conducted to test the effects of gender and number of followers on perceived credibility while incorporating covariates with a significant correlation and relevant relationship with the outcome variable: credibility (Table 2-3). Based on previous research, existing literature and correlation significance, the covariates that were included in the ANCOVA were Gender of the respondent, News Skepticism, Twitter Frequency and Social Attraction. There was no main effect of Gender (F (1, 190) = .401, p > .05), or Follower Count (F (1, 190) = .069,

p > .05), on perceived credibility, H1 and H2 were not supported. A significant interaction did not exist between gender and number of followers on perceptions of credibility (F (1, 190) = 1.66, p > .05) meaning H3 was also not supported . In other words, when Twitter frequency, news skepticism, social attraction, and gender of the respondent are all considered, there is no effect on the perceived credibility of a journalist based on his or her follower count and his or her gender. Social attraction (F (1, 190) =

47.51, p < .01), Twitter frequency (F (1, 190) = 4.94, p < .05) and News skepticism (F (1,

190) = .04, p < .05) each had a significant influence on perceived credibility score.

Table 2

Descriptive Statistics for Perceived Credibility Score Journalist Gender # of Twitter Mean Std Deviation N Followers Male High 22.52 8.96 53

Low 25.09 9.64 49

Total 23.76 9.34 102

Female High 26.54 9.84 52

Low 22.98 10.25 44

Total 24.31 9.72 96

Table 3

ANCOVA Summary Table for Perceived Credibility Score Source df MS F p Effect Size

News Skepticism 1 290.23 4.47 .04 .023

Twitter Freq. 1 321.10 4.94 .03 .025

Social Attraction 1 3085.58 47.51 .00 .200

Gender (Respondent) 1 95.99 1.48 .23 .008

Gender (Journalist) 1 26.05 .40 .53 .002

# of Twitter Followers 1 4.49 .07 .79 .000

Gender * # of Twitter Followers 1 107.58 1.66 .20 .009

Within groups 190 64.95

Total 198

Note.—MS = Mean squares, effect size = partial η2.

While credibility is made up of both competence and trustworthiness, this researcher thought it was relevant to break down the dimensions of credibility in order to further analyze if any particular dimension impacted credibility perceptions. A separate analysis with Competence and Trustworthiness respectively were conducted in order to test for these relationships.

When the ANCOVA was run with Competence as the dependent variable (Table

4-5), there was no main effect of Gender (F (1, 192) = .62, p > .05), or Follower Count (F

(1, 192) = .51, p > .05), on perceived competence and a significant interaction did not exist between gender and number of followers on perceptions of competence (F (1, 192)

= .27, p > .05). News skepticism (F (1, 192) = .25, p > .05) and Twitter frequency (F (1,

192) = 2.63, p > .05) were not significant, but Gender of the respondent (F (1, 192) =

4.26, p < .05) and Social attraction (F (1, 192) = 38.45, p < .01) each had a significant influence on perceived competence score.

Table 4

Descriptive Statistics for Perceived Competence Score Journalist Gender # of Twitter Mean Std Deviation N Followers Male High 4.75 .99 53

Low 4.96 1.04 49

Total 4.85 1.02 102

Female High 5.07 1.07 53

Low 4.84 .98 45

Total 4.97 1.04 98

Table 5 ANCOVA Summary Table for Perceived Competence Score Source df MS F p Effect Size

News Skepticism 1 .21 .25 .62 .001

Twitter Freq. 1 2.16 2.63 .11 .014

Social Attraction 1 31.55 38.45 .00 .167

Gender (Respondent) 1 3.49 4.26 .04 .022

Gender (Journalist) 1 .51 .62 .43 .003

# of Twitter Followers 1 .42 .51 .48 .003

Gender * # of Twitter Followers 1 .22 .27 .61 .001

Within groups 192 .82

Total 200

Note.—MS = Mean squares, effect size = partial η2.

When the ANCOVA was run with Trustworthiness as the dependent variable

(Table 6-7), there was no main effect of Gender (F (1, 191) = .28, p > .05), or Follower

Count (F (1, 191) = .07, p > .05), on perceived trustworthiness, but a marginally significant interaction did exist between gender and number of followers on perceptions of trustworthiness (F (1, 191) = 2.94, p = .09) (Figure 5). Gender of the respondent (F (1,

191) = .37, p > .05) was not significant, but News skepticism (F (1, 191) = 8.01, p < .01) and Social attraction (F (1, 191) = 37.76, p < .01) each had a significant influence on perceived trustworthiness while Twitter frequency (F (1, 191) = 3.54, p = .06) was marginally significant.

Table 6 Descriptive Statistics for Perceived Trustworthiness Score Journalist Gender # of Twitter Mean Std Deviation N Followers Male High 4.60 .94 53

Low 4.89 1.10 49

Total 4.74 1.03 102

Female High 5.09 1.10 52

Low 4.59 1.20 45

Total 4.86 1.17 97

Table 7

ANCOVA Summary Table for Perceived Trustworthiness Score

Source df MS F p Effect Size

News Skepticism 1 6.79 8.01 .01 .040

Twitter Freq. 1 3.00 3.54 .06 .018

Social Attraction 1 32.01 37.76 .00 .165

Gender (Respondent) 1 .32 .37 .54 .002

Gender (Journalist) 1 .23 .28 .60 .001

# of Twitter Followers 1 .06 .07 .79 .000

Gender * # of Twitter Followers 1 2.49 2.94 .09 .015

Within groups 191 .85

Total 199

Note.—MS = Mean squares, effect size = partial η2.

Figure 5. ANCOVA Interaction – Trustworthiness. Interaction between gender and number of Twitter followers on perceived trustworthiness score with covariates Gender of respondent, Social Attraction, Twitter Frequency and News Skepticism.

Because social attraction was a significant predictor throughout the analyses, this researcher thought it was important to complete an additional ANOVA (Table 8-9) analysis with social attraction as the dependent variable in order to see whether or not it had a significant relationship with the independent variables. There was no main effect of

Gender (F (1, 196) = .20, p > .05), or Follower Count (F (1, 196) = 1.07, p > .05), on perceived social attraction, but a significant interaction did exist between gender and number of followers on perceptions of social attraction (F (1, 196) = 4.17, p < .05)

(Figure 6).

Table 8 Descriptive Statistics for Perceived Social Attraction Journalist Gender # of Twitter Mean Std Deviation N Followers Male High 4.32 1.19 53

Low 4.48 .84 49

Total 4.40 1.04 102

Female High 4.72 1.23 53

Low 4.23 1.18 45

Total 4.49 1.23 98

Table 9 ANOVA Summary Table for Perceived Social Attraction Source df MS F p Effect Size

Gender 1 .25 .20 .66 .001

# of Twitter Followers 1 1.35 1.07 .30 .005

Gender * # of Twitter Followers 1 5.28 4.17 .04 .021

Within groups 196 1.27

Total 200

Note.—MS = Mean squares, effect size = partial η2.

Figure 6. Interaction between gender and number of Twitter followers on perceived Social Attraction score.

Chapter 6: Discussion

This research study began with a research question regarding existing cues, such as number of followers and perceived gender and how they might affect credibility perceptions for journalists on social media. After reviewing related literature and conducting an experiment, this study aimed to contribute new insights regarding journalists on Twitter and audience perceptions of their credibility. As media companies and news outlets continue to expect reporters to utilize social media in their work, it is important to consider the effects of their social media presence on audience perceptions.

As, Hu (2015) noted; “One of the most important elements in communication effect is recipient. Whether the recipient accepts the information, or how s/he perceives the information, sender has an enormous impact on the communication effect” (p. 145).

Not only did this research set out to help understand the role social media profiles of journalists play on establishing credibility with audiences, but it specifically focused on offering insights about the effect digital and social cues, like number of followers and gender, have on audience perceptions of credibility online.

There was no main effect of gender or the number of followers on the perceived credibility score in any of the ANCOVA analyses. While the original ANCOVA analysis with credibility as the dependent variable did not produce significant findings, there were still several interesting takeaways from the results that are worth consideration.

In the ANCOVA with credibility set as the dependent variable, Twitter frequency and news skepticism had a significant effect on the perception of a journalist’s credibility

based on the number of followers he or she had and his or her gender. In other words, a respondent’s Twitter use frequency score, news skepticism score and perceived social attraction score were all significantly associated with higher perceptions of credibility.

When the competence dimension of credibility was added as an outcome in the

ANCOVA, the dummy coded gender of the respondent became significant which suggests that being female was significantly associated with higher perceptions of competence. In other words, female respondents evaluated the competence of each journalist higher than male respondents. Twitter frequency and news skepticism were not significant covariates on perceived competence of a journalist based on his or her Twitter followers and his or her gender.

In the last ANCOVA where trustworthiness was added as the dependent variable, gender of the respondent was not significant, but news skepticism was, and Twitter frequency became marginally significant. The most interesting finding, however, was the marginally significant interaction effect between gender of the journalist and follower count. This interaction effect suggests that the number of followers a female journalist has on Twitter has an effect on her perceived trustworthiness while for a male journalist, follower count does not affect whether people perceive him as being trustworthy or not.

Therefore, the impact of the number of followers a journalist has on Twitter on perceptions of trustworthiness depends on the gender of that journalist. Previous research has determined, people are increasingly susceptible to misinformation online and on social media specifically, making trust an incredibly important element for journalists to

maintain especially in order to differentiate authentic news sources from sources of disinformation. As Shao et al. (2018), writes, “Bots can tailor misinformation and target those who are most likely to believe it, taking advantage of our tendencies to attend to what appears popular, to trust information in a social setting, and to trust social contacts,”

(p.2). This finding regarding perceived trustworthiness and gender aligns with previous research surrounding gender and audience perceptions offline and is an important one to discuss further.

Considering recent movements like that of #MeToo which began on social media, it is worth considering how the findings from this research focused on gender differences and credibility perceptions for journalists can relate to a larger discussion surrounding the way women are treated and perceived online. If women are discouraged because they are not given an equal trust evaluation as their male colleagues online, the way they perform professionally online may reflect that understanding of difference. As Dhrodia (2018) notes, “…the risk is that women will self-censor themselves online, refrain from engaging on certain subject[s] or in political activism, or will choose to leave social media platforms altogether” (p. 385). Because women make up over half of news analysts and reporters in the U.S. (U.S. Bureau of Labor Statistics, 2018), and actively use Twitter to report on stories, interact with sources and keep up with current trends and topics, their relationship with social media is important to understand. The results of this study may have produced more significant findings with higher power and could then suggest that the number of followers a journalist has on Twitter may impact how a

consumer evaluates them as being trustworthy or not, but only if that journalist is perceived to be female. If audiences on Twitter evaluate journalists’ credibility by using cues like the number of followers they have, then it is important to consider financial incentives attached to maintaining a high follower count on the platform where “virtually any industry where a mass audience – or the illusion of it – can be monetized”

(Confessore et al., 2018, p. 18).

For online influencers, public figures and celebrities, there is a clear incentive for gaining more followers on social media platforms, as advertising opportunities are most often presented to accounts with high engagement because, “the more people influencers reach, the more money they make” (Confessore et al., 2018, p. 25). It is likely that hiring managers or executives at media companies with online advertising concerns may be encouraged to hire or promote journalists with high follower counts on Twitter because there is an existing audience, or following, that already perceives them as being a trustworthy source. The reported bonus structure for new hires at the Athletic suggests that a journalist with just 100,000 followers on Twitter that can convert 10%, of their followers would be eligible to receive nearly a $400,000 bonus (Gordon, 2018), presenting a clear financial gain connected to a journalists’ follower count. Another aspect to consider is that women are already occupying less positions of power in the media industry (De Vuyst, 2017), which means men are more likely to make hiring decisions and women may be encouraged to find ways to even the playing field. This poses obvious ethical concerns, especially if what the findings of this research study

suggest about gender differences were to be generalized to a larger population. What will discourage incidents like the one in 2018 – where journalists were caught having paid for followers on Twitter (Confessore et al., 2018) from happening again, especially if there is a clear financial incentive to gain more followers for journalists, and as the results from this study suggest, specifically for women in the industry?

Another important takeaway from the analyses is that social attraction had a consistently significant relationship with credibility and its dimensions. This suggests that whether or not a respondent found the journalist socially attractive was a significant predictor in his or her assessment of that journalist’s credibility, which aligns with previous research (McCroskey & McCain, 1974; McCroskey, 1999). Social attraction is closely related to credibility and due to this relationship, it has been incorporated in past credibility research focused on offline interaction. The credibility measurement for this particular study did not include social attraction as trustworthiness and competence were both consistent and reliable measures in previous research which could also be applied to credibility online. Social attraction, however, has been incorporated in credibility studies alongside other variables such as interpersonal interaction and caring measurements, both of which require familiarity with the individual whose credibility is being evaluated.

While the social attraction variable was not considered as a primary predictor variable in this study, it was included as a covariate to understand if social attraction influenced people’s perceptions of online personas on Twitter. The results from this study suggest that perceived social attraction is significantly related to the gender of a journalist and the

number of followers he or she has on Twitter. This finding also suggests that social attraction may be a relevant dimension of credibility measurements online and future research should consider incorporating social attraction in order to better distinguish an accurate measurement of credibility for social media applications. Future research findings may be able to offer a new definition for source credibility online with an adapted credibility measurement as well.

Limitations and Future Research

There are several limitations that come to mind regarding the scope of this research study. Firstly, a larger sample size would have benefited the reliability of the findings but due to the constraints of resources, such as a limited budget and time, the sample size was kept to a rather manageable number. Future studies should replicate this research with a larger audience. With more power, many of the results of this study may have been significant such as that of the interaction effect between gender and the number of followers a journalist had on perceptions of trustworthiness and gender of the respondent on perceived competence. While the results suggest that a marginally significant relationship exists between gender and number of followers on perceived trustworthiness, when credibility overall is considered, the results no longer indicate that the relationship is significant, which is what this study sought to measure. Perhaps replicating the study with a larger and more diverse sample might mitigate this issue and provide more confidence in the findings.

Demographically speaking, age was a question unintentionally omitted from the questionnaire and the data could have been valuable in understanding the results. Future research regarding this topic should make sure to collect age data from respondents. It also may be worth taking into account the perceived age of the journalists shown in the experiment as well. Data was collected regarding race, but it was not a variable this researcher set out to consider. Race is another demographic element, however, that would be interesting to study in its relation to credibility perceptions of journalists on Twitter.

Amazon Mechanical Turk (MTurk) offered several benefits for conducting this study by allowing parameters to be set, establishing that each respondent had a Twitter account while it also provided a timely response. The service also allowed for a more diverse sample of respondents as opposed to a sample that targets university students which previous research has also determined (Buhrmester, Kwang & Gosling, 2011).

However, the survey tool may have also presented several limitations to this study that are important to consider. Firstly, MTurk workers are likely to be more technologically advanced than the general population as it is a web-based service requiring technology

(i.e. laptop, computer or tablet) in order for it to be accessed. However, given the nature of the current research, this should not have acted as a barrier. Similarly, MTurk may represent a younger sample of the population and socioeconomic diversity may also be limited due to those same technology requirements. Future research should consider asking respondents to indicate their location (city, state) in order to confirm geographical diversity in respondents.

As with any experiment where created personas are utilized, researchers must question how responsive participants were to the created stimuli and whether or not they were comparable enough to the authentic version respondents are familiar with. While differences in the platform aesthetic were mitigated to the best of this researcher’s ability, the perception from participants is worth considering. The limitation regarding the stimuli was expounded by the fact that the template used to create the profiles for each journalist represented the desktop version of Twitter, rather than the mobile version, but respondents were still able to participate from mobile devices. This is an important consideration as well because, as noted in previous chapters, the mobile version of

Twitter is where most Twitter users reside (Smith, 2019). This could have affected the way respondents evaluated the stimuli based on familiarity, if they are less familiar with the desktop version of the platform, they may have questioned the accuracy of the stimuli or simply took more time to look for cues in helping assess their credibility perceptions.

Furthermore, the elements of the stimuli could have posed as limitations, specifically the images used to represent each persona. While the images were chosen at random and were selected from the “white” race category, individual preference or race and gender interpretations could all have affected the way any particular respondent evaluated each persona.

While considering the familiarity of respondents with Twitter’s platform, it is also worth noting that the journalist persona profiles were not shown to have a verification badge. The image of the verification badge is intentionally hard to copy or imitate as this

researcher considered trying to incorporate it. This researcher felt that an imitated version of the badge would potentially lead to respondents evaluating the journalists’ credibility based on the badge and whether or not it looked authentic or not. One does have to question whether or not the absence of the badge was considered by respondents and whether or not the badge itself represents credibility to them. This is a consideration that future research should consider in order to further test the dynamics of Twitter and the existing attributes that may influence perceptions of credibility.

Lastly, while the names and images of each journalist persona were set as representing a male and female, respondents may not have perceived them as such.

Perceived gender itself is a highly researched topic and as it pertains to this research, it cannot be assumed that each gender was perceived as what it was created to represent as there are not mandatory or distinct gender markers on the platform or on the profiles that were created. With that, race was another element that was taken into account while creating the stimuli. While they were both selected from the “white” categories in their respective gender, it cannot be assumed that the respondents perceived them as being white. Both characteristics are worth considering when conducting future research in order to mitigate biases and misinterpretation.

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Appendix A

Twitter Verified Media Members Table

Acct Name Outlet/Org Followers Following Tweets @amandamoyerWSB Amanda Moyer WSB Radio 2,164 342 1,282 @LisaDNews Lisa Desjardins PBS 52,800 1,065 24,500 Westwood One @CostantiniWW1 Bob Costantini News Radio 1,520 78 24,200 @jimroopeNEWS Jim Roope N/A 1,527 197 2,262 @dickulianoCNN Dick Uliano WTOP 1,047 0 198 Steve Westwood One @SKastenbaum Kastenbaum News 7,648 709 19,700 @DanaBashCNN Dana Bash CNN 388,700 1,195 10,900 @AlexMLeo Alex Leo Daily Beast 29,500 3,387 91 @jimrome Jim Rome N/A 1,300,000 235 23,200 @JessicaYellin Jessica Yellin N/A 103,000 6,180 4,563 @JennaWolfe Jenna Wolfe Fox Sports 171,400 737 6,706 @ebertchicago Roger Ebert N/A 730,100 719 34,900 @TweetTonyHarris Tony Harris Discovery 9,123 851 5,513 @TomJackson57 Tom Jackson ESPN 58,400 27 71 Christiane @camanpour Amanpour CNN 2,900,000 186 11,000 @edhornick Ed Hornick Yahoo News 5,683 6,852 660 @RoeConn Roe Conn Chicago WGN Radio 16,800 790 3,000 Thomas L. @tomfriedman Friedman NY Times 888,300 52 812 @maureendowd Maureen Dowd NY Times 685,200 1,301 1,295 Campbell @campbell_brown Brown Facebook 22,700 2,469 5,204 @ColinCowherd Colin Cowherd Fox Sports 1,400,000 727 27,000 Michelle @MichelleDBeadle Beadle N/A 1,500,000 3,050 25,900 @AdamSchefter Adam Schefter ESPN 7,400,000 3,023 45,700 @chucktodd Chuck Todd NBC News 2,100,000 1,863 50,500 @SamFeistCNN Sam Feist CNN 49,100 268 1,913 @brikeilarcnn Brianna Keilar CNN 136,300 1,137 5,705 @tvkatesnow Kate Snow NBC News 79,300 646 16,900 @FareedZakaria Fareed Zakaria CNN 933,500 423 8,153 @seanhannity Sean Hannity Fox News 4,200,000 4 549

Twitter Verified Media Members Table Continued

Knight @JenniferPreston Jennifer Preston Foundation/NY Times 99,300 6,130 6,191 @JoshLevs Josh Levs UN 38,900 14,200 36,900 @BarbaraJWalters Barbara Walters ABC News 1,600,000 16 544 @wingoz Trey Wingo N/A 988,100 566 85,900 @JohnKingCNN John King CNN 313,000 1,771 7,032 @erichippeau Eric Hippeau The Huffington Post 33,800 909 4,471 @BPenningtonNYT Bill Pennington NY Times 5,450 21 39 Chris @mortreport Mortensen ESPN 2,300,000 3,277 22,400 Rosemary @rosemaryCNN Church CNN 45,200 4,202 38,300 @Kenny_Mayne Kenny Mayne N/A 333,200 1,959 32,200 @soledadobrien Soledad O'Brien Starfish Media Group 1,100,000 450,400 75,400 @RobMarciano Rob Marciano ABC News 255,100 778 18,200 Suzanne @SuzanneMalveaux Malveaux CNN 108,100 10,900 3,210 @AlvaroGarnero Alvaro Garnero RecordTV 614,700 1,412 34,100 @HalaGorani Hala Gorani CNN 204,300 980 13,500 @kiranchetrytv Kiran Chetry N/A 23,500 1,895 5,016 @kingsthings Larry King N/A 2,500,000 902 16,400 @shaunrobinson Shaun Robinson N/A 100,400 825 40,000 @JennBrown Jenn Brown MMA 192,600 1,103 13,400 Courtney @courtneyhazlett Hazlett N/A 9,624 446 3,449 @LukeRussert Luke Russert N/A 250,300 2,827 41,600 @latimesmuskal Michael Muskal N/A 3,522 1,269 38,200 Norah @NorahODonnell O'Donnell CBS News 207,800 2,289 18,200 @danielshea Danny Shae N/A 4,612 761 2,305 @johnrobertsFox John Roberts Fox News 117,300 466 5,967 @MatthewBerryTMR Matthew Berry ESPN 1,000,000 2,995 58,800 @nancyodell Nancy O'Dell ET 419,300 35,800 12,000 @AnnCurry Ann Curry N/A 1,500,000 1,603 6,258 @BDUTT Barkha Dutt Washington Post 7,000,000 2,131 126,800 @NicoleLapin Nicole Lapin N/A 57,700 968 22,400

Twitter Verified Media Members Table Continued

@ProducerGuy1 BillWolff N/A 7,476 806 1,395 @AWMooneyDC Alexander Mooney CNN 9,913 1,067 2,198 @charltonbrooker Charlie Brooker N/A 1,200,000 543 10,600 @PerezHilton Perez Hilton PerezHilton.com 6,000,000 570 320,300 @elizcohencnn Elizabeth Cohen CNN 20,600 5,189 1,903 @JesseRodriguez Jesse Rodriguez MSNBC 31,000 9,080 23,500 @vikramchandra Vikram Chandra editorji.com 19,500 90 21,900 @jensabella Jen Sabella TheGirlTalk 7,846 2,927 13,100 @HowardBeck Howard Beck Bleacher Report 190,800 1,363 77,100 Natalie P. @frugalista McNeal N/A 11,200 4,723 46,500 @paulkrugman Paul Krugman NY Times 4,500,000 47 15,900 @NickKristof Nicholas Kristof NY Times 2,000,000 2,072 40,400 @gailcollins Gail Collins NY Times 62,900 25 660 @Betty_Nguyen Betty Nguyen N/A 31,700 253 7,592 @AliVelshi Ali Velshi MSNBC 400,400 7,617 32,800 @edhenry Ed Henry Fox News 346,700 8,555 31,000 @tjholmes T.J. Holmes ABC News 138,500 23 33,200 Greta Van @greta Susteren N/A 1,200,000 2,421 159,600 @MediaJonKlein Jon Klein N/A 5,146 289 461 @RickSanchezTV Rick Sanchez RT/Fox News 140,500 65,900 21,800 @michaelpfalcone Michael Falcone AtlanticLIVE 34,400 3,200 8,386 @sarahaines Sara Haines ABC News 552,700 2,687 6,205 @jackcafferty Jack Cafferty CNN 29,500 39 1,796 @DanielleTV Danielle Dellorto NBC News 30,300 1,629 8,712 @octavianasr Octavia Nasr CNN 1,700,000 1,604 39,200 @brianstelter Brian Stelter CNN 655,800 6,515 183,400 @AHMalcolm Andrew Malcolm McClatchy Newspapers 146,500 57,500 80,500 Catherine @crampell Rampell Washington Post 157,800 1,183 56,800 @nicopitney Nico Pitney NowThisNews 20,700 22,800 1,842 @JuliaAllison Julia Allison N/A 51,700 364 25 @MMStewartNews Martina Stewart USA Today 10,700 5,688 48,600 @benparr Ben Parr N/A 62,100 2,483 2,557 @karaswisher Kara Swisher N/A 1,300,000 1,729 121,900 @PeterHamby Peter Hanby Vanity Fair 87,000 7,844 23,300 @raywert Ray Wert N/A 19,900 6,339 27,800 @khoi Khoi Vinh N/A 326,200 2,903 113

Appendix B

Instruments

Measure of Credibility (McCroskey & Teven, 1999)

Competence Intelligent 1...... 2...... 3...... 4...... 5...... 6...... 7 Unintelligent Untrained 1...... 2...... 3...... 4...... 5...... 6...... 7 Trained Inexpert 1...... 2...... 3...... 4...... 5...... 6...... 7 Expert Informed 1...... 2...... 3...... 4...... 5...... 6...... 7 Uninformed Incompetent 1...... 2...... 3...... 4...... 5...... 6...... 7 Competent Bright 1...... 2...... 3...... 4...... 5...... 6...... 7 Stupid

Trustworthiness

Honest 1...... 2...... 3...... 4...... 5...... 6...... 7 Dishonest Trustworthy 1...... 2...... 3...... 4...... 5...... 6...... 7 Untrustworthy Honorable 1...... 2...... 3...... 4...... 5...... 6...... 7 Dishonorable Moral 1...... 2...... 3...... 4...... 5...... 6...... 7 Immoral Unethical 1...... 2...... 3...... 4...... 5...... 6...... 7 Ethical Phony 1...... 2...... 3...... 4...... 5...... 6...... 7 Genuine

Social Attraction (McCroskey & McCain, 1974)

Strongly Neither Agree Strongly

Disagree Nor Disagree Agree I think this journalist could be a friend of mine. 1...... 2...... 3...... 4...... 5...... 6...... 7

It would be difficult to talk with them. 1...... 2...... 3...... 4...... 5...... 6...... 7

They wouldn’t fit into my circle of friends. 1...... 2...... 3...... 4...... 5...... 6...... 7

We could never establish a personal friendship 1...... 2...... 3...... 4...... 5...... 6...... 7 with each other. I would like to have a friendly chat with them. 1...... 2...... 3...... 4...... 5...... 6...... 7

Social Media Usage

These next set of questions enquire about your social media usage.

Never Many times a day How frequently do you use Twitter, overall?

0...... 1...... 2...... 3...... 4...... 5

How frequently do you send tweets about 0...... 1...... 2...... 3...... 4...... 5 yourself?

0...... 1...... 2...... 3...... 4...... 5 How frequently do you share other’s tweets?

Using your closest guess based on your memory, please answer the following two questions: 73

Please indicate how many Twitter followers you have:______

Please indicate, on average, how many accounts you follow on Twitter:______

News Media Literacy (Maksl, Ashley & Craft, 2015)

Please indicate how much you agree or disagree with each statement

Automatic vs. Mindful Thought Processing:

Strongly Neither Agree Strongly

Disagree Nor Disagree Agree I don’t like to have to do a lot of thinking 1...... 2...... 3...... 4...... 5...... 6...... 7

I try to avoid situations that require thinking in depth 1...... 2...... 3...... 4...... 5...... 6...... 7 about something I prefer to do something that challenges my thinking abilities rather than something that requires little 1...... 2...... 3...... 4...... 5...... 6...... 7 thought I prefer complex to simple problems 1...... 2...... 3...... 4...... 5...... 6...... 7

Thinking hard and for a long time about something 1...... 2...... 3...... 4...... 5...... 6...... 7 gives me little satisfaction

Media Locus of Control: Strongly Neither Agree Strongly

Disagree Nor Disagree Agree If I am misinformed by the news media, it is my own behavior that determines how soon I will learn credible 1...... 2...... 3...... 4...... 5...... 6...... 7 information

I am in control of the information I get from the news media 1...... 2...... 3...... 4...... 5...... 6...... 7

When I am misinformed by the news media, I am to blame 1...... 2...... 3...... 4...... 5...... 6...... 7

The main thing that affects my knowledge about the world is what I myself do. 1...... 2...... 3...... 4...... 5...... 6...... 7

If I pay attention to different sources of news, I can avoid being misinformed 1...... 2...... 3...... 4...... 5...... 6...... 7

If I take the right actions, I can stay informed. 1...... 2...... 3...... 4...... 5...... 6...... 7

News Media Knowledge Structures 1. Most media outlets in the United States are: a.) For-profit business (correct); b.) Owned by the government; c.) Non-profit businesses; d.) Don’t know

2. If you wanted to get a job as a news reporter in the US, you would need to get a license from… a.) The Federal Communications Commission; b.) The Federal Trade Commission; c.) Society of Professional Journalists; d.) News reporters are not required to be licensed (correct); e.) Don’t know

3. In 1983, around 50 companies owned most of the media outlets Americans consumed. How many companies own most of the media we consume today? a.) 100; b.) 50; c.) 25; d.) 5 (correct); e.) Don’t know

4. Which of the following cable news networks is generally thought to have a politically conservative bias? a.) CNN; b.) Fox News (correct); c.) MSNBC; d.) MTV News; e.)Don’t know

5. Which of the following news outlets does NOT depend primarily on advertising for financial support? a.) CNN; b.) PBS (correct); c.) The New York Times; d.) Newsweek magazine; e.) Don’t know

6. When it comes to reporting the news, the main difference between a website like Google News and a website like CNN.com is that: a.) Google does not have reporters who gather information, while CNN does (correct); b.) Google focuses on national news, while CNN focuses on local news; c.) Google has more editors than CNN does; d.) Google charges more money for news than CNN does; e.) Don’t know

7. Who has the most influence on what gets aired on the local TV news? a.) Individual reporters; b.) The anchor, the person reading the news; c.) The cameraman; d.) The producer/editor (correct); e.) Don’t know

8. The amount of racial/ethnic minority coverage in the news: a.) Accurately reflects the proportion of minorities in the U.S. population; b.) Under-represents reflects the proportion of minorities in the U.S. population (correct); c.) Over-represents reflects the proportion of minorities in the U.S. population; d.) Don’t know

9. Coverage of election campaigns in the news usually centers on: a.) Who’s winning (correct); b.) In-depth analysis of where candidates stand on the issues; c.) The candidates’ educational backgrounds; d.) Don’t know

10. One common criticism of the news is that it is not objective. What do people who make that criticism typically mean by it? a.) The reporter gives only the facts about the story; b.) The reporter puts his or her opinion in the story (correct); c.) The reporter’s story relies too much on the opinions of people who are neutral; d.) The reporter doesn’t make the purpose of the story clear; e.) Don’t know

11. Writing a press release is typically the job of: a.) A reporter for CNN.com; b.) A spokesperson for Coca-Cola (correct); c.) A lawyer for Yahoo!; d.) A producer for NBC Nightly News; e.) Don’t know

12. Most people think the news has: a.) A greater effect on themselves than other people; b.) A greater effect on other people than themselves (correct); c.) The same effect on themselves as others; d.) Does not have any effects on anyone; e.) Don’t know

13. People who watch a lot of television news often tend to think the world is: a.) More violent and dangerous than it actually is (correct); b.) Less violent and dangerous than it actually is; c.) Just as violent and dangerous as it actually is; d.) Don’t know

14. If a topic gets a lot of coverage in the news, people who pay attention to the news are: a.) More likely to think the topic is important (correct); b.) Less likely to think the topic is important; c.) Neither more nor less likely to think the topic is important; d.) Don’t know

15. Most news outlets depend on advertising to make money. What is a possible effect of this? a.) News could encourage people to buy things they don’t need; b.) News could emphasize things that aren’t really important; c.) All of the above (correct); d.) None of the above. There are no effects; e.) Don’t know

Measuring Outcome Variables Motivations for News Consumption: Strongly Neither Agree Strongly

Disagree Nor Disagree Agree I don’t see what news does for me 1...... 2...... 3...... 4...... 5...... 6...... 7

I follow the news because I’m supposed to 1...... 2...... 3...... 4...... 5...... 6...... 7

I follow the news for my own good 1...... 2...... 3...... 4...... 5...... 6...... 7

I follow the news because I like to 1...... 2...... 3...... 4...... 5...... 6...... 7

News Media Skepticism:

Strongly Neither Agree Strongly

Disagree Nor Disagree Agree I think the news media are fair 1...... 2...... 3...... 4...... 5...... 6...... 7

I think the news media tell me the whole story 1...... 2...... 3...... 4...... 5...... 6...... 7

I think the news media are accurate 1...... 2...... 3...... 4...... 5...... 6...... 7

I don’t think the news media can be trusted 1...... 2...... 3...... 4...... 5...... 6...... 7

I think the news media prioritize being first to report a story 1...... 2...... 3...... 4...... 5...... 6...... 7

I think the news media get in the way of society solving its 1...... 2...... 3...... 4...... 5...... 6...... 7 problems I trust the media to report the news fairly 1...... 2...... 3...... 4...... 5...... 6...... 7

I have confidence in the people running the institutions of the 1...... 2...... 3...... 4...... 5...... 6...... 7 press

News Media Use 1. On a typical weekday, do you read a daily newspaper? If yes, about how much time do you spend reading a daily print newspaper on a typical weekday? 2. On a typical weekday, do you watch the news or any news programs on television? If yes, about how much time do you spend watching the news or any news programs on television on a typical weekday? 3. On a typical weekday, do you listen to the news or any news programs on radio? If yes, about how much time do you spend listening to the news or any news programs on the radio on a typical weekday? 4. On a typical weekday, do you get any news online through the Internet?

If yes, about how much time do you spend getting news online on a typical weekday?

News Affinity (Rubin, 1981) Strongly Neither Agree Strongly

Disagree Nor Disagree Agree Reading news is one of the most important things I do every day 1...... 2...... 3...... 4...... 5...... 6...... 7

If I could not read news, I would really miss it 1...... 2...... 3...... 4...... 5...... 6...... 7

Reading news is very important in my life 1...... 2...... 3...... 4...... 5...... 6...... 7

I cannot go without reading news for several days 1...... 2...... 3...... 4...... 5...... 6...... 7

I would feel lost without reading news 1...... 2...... 3...... 4...... 5...... 6...... 7

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