The Social Transmission of User Generated Memes

The Social Transmission of User-Generated Memes

Ian Dennis Miller

A thesis submitted in conformity with the requirements for the degree of Master of Arts Graduate Department of Psychology University of Toronto

The Social Transmission of User-Generated Memes

Ian Dennis Miller Master of Arts Graduate Department of Psychology University of Toronto 2012

The popular concept of viral media is like the ﬂu: once unleashed, it naturally infects all your friends. This work suggests that viral impact may not be determined by the content alone but also by the content’s creator as part of the Viral Feedback Loop. Participants interacted with a type of online meme called an image macro which has historically been shared virally. Participants made their own User-Generated Content (UGC) with Memelab, an image macro builder written for this experiment. Participants then shared their UGC online, which was longitudinally monitored to create a behavioural measure of viral impact. When sharing with a friend, participants’ predictions of how much their UGC would be liked was positively associated with viral impact. Intent to Share was modelled as a function of image macro content features and participant responses, which was then modelled with an Agent-based computer simulation.

ii Acknowledgements

Professors George Cree and Jason Plaks for overseeing the defence of this thesis.

SPA Community which is a great group – the sort I like to be involved with. I ﬁrst presented this work at a SPA meeting, and the positive response I received inspired me to persevere.

Michelle Hilscher who patiently answered all my questions and helped me navigate these waters, and who made it easy for me to ﬁt into the lab.

Open Source Community which provided so many resources used in this research, including: GNU/Linux, Python, R and R Studio, nginx, LaTeX, Eclipse, Chromium, and git1.

Visionaries Dr. Linus Torvalds, Richard Stallman, Sir Tim Berners-Lee, Buckminster Fuller, and William Gibson, who have either made their ideas real in this world, or envisioned a world that justiﬁes experimentation.

Family and Friends who have always been there.

Professor Elizabeth Page-Gould whose statistics lessons were so valuable and whose support was invaluable.

Professor Gerry Cupchik for cultivating a rare and vital environment. Thank you for reaching out and sharing your vision.

1This document was generated from syntax revision 4a83ceb.

iii Contents

1 Introduction 1 1.1 Memes, Viral Videos, and Image Macros ...... 2 1.2 Online Behaviour ...... 4 1.3 Viral Feedback Loop ...... 5 1.4 The Present Study ...... 7

2 Methods 8 2.1 Participants ...... 8 2.2 Materials ...... 8 2.2.1 Background Questionnaires ...... 8 2.2.2 Stimulus Library ...... 9 2.2.3 Memelab ...... 12 2.3 Procedure ...... 13 2.3.1 Part One: Background and Image Macro Baseline ...... 13 2.3.2 Part Two: Content Creation Task with Memelab ...... 14 2.3.3 Longitudinal Monitoring ...... 16 2.3.4 Transaction Log Analysis ...... 17 2.4 Agent-Based Simulation ...... 17

3 Results 20 3.1 Scoring ...... 21 3.1.1 Creator/Consumer Scale ...... 21 3.1.2 Internet Motivations ...... 22 3.1.3 Self Factors ...... 24 3.2 Image Macros: Baseline and User-Generated Content ...... 25 3.3 Model Investigation ...... 26 3.3.1 Predicting Intention to Share ...... 26 3.3.2 Predicting Viral Impact ...... 27

iv 3.3.3 A Model Incorporating Content ...... 28 3.3.4 Model Comparison ...... 29 3.4 Simulation ...... 30 3.5 Exploratory Analysis ...... 31

4 Discussion 33 4.1 Viral Feedback Loop and Simulation ...... 34 4.2 Memelab as a Platform ...... 34 4.3 College Memes ...... 34 4.4 Future Directions ...... 35 4.4.1 Increased Simulation Realism ...... 35 4.4.2 Further Disambiguation of Content and Creator ...... 36 4.4.3 Conﬁrming the Exploratory Work ...... 36 4.5 Conclusion ...... 36

Bibliography 37

v List of Tables

3.1 Creator/Consumer factors and item loadings ...... 22 3.2 Internet Motivations factors and items loadings ...... 23 3.3 Self Survey factors and items loadings ...... 25 3.4 Best Model Selection ...... 32

vi List of Figures

1.1 A low self-relevance image macro created by a participant with Memelab.1 1.2 The cat that launched an empire: I can has cheezburger? ...... 3 1.3 Model of Sharing, including the Viral Feedback Loop...... 6 1.4 The Viral Feedback Loop algorithm ...... 7

2.1 Baseline image macro - high relevance ...... 10 2.2 Baseline image macro - low relevance ...... 11 2.3 Baseline Image Macro items ...... 11 2.4 Selecting a background image ...... 12 2.5 White-label survey embedded within meme8.com website ...... 13 2.6 Captioning an image macro ...... 15 2.7 Sharing an image macro via http://meme8.com ...... 16 2.8 A line from the web server access log ...... 16 2.9 Simulation of Viral Feedback Loop with Repast, 80 iterations ...... 18 2.10 An agent decides whether or not to share ...... 19

3.1 Frequency of Viral Impact ...... 20 3.2 Creator/Consumer Scale - Eigenvalue scree plot ...... 21 3.3 Motivations for Internet Use - Eigenvalue scree plot ...... 23 3.4 Self Factors - Eigenvalue Scree Plot ...... 24 3.5 A high self-relevance image macro created by a participant with Memelab. 26 3.6 Multilevel regression of participants’ likelihood of sharing 8 baseline memes, represented graphically ...... 27 3.7 A model of web content hits using proximal and distal predictors . . . . . 28 3.8 Structural Equation Model with a latent construct representing content . 29 3.9 Simulation of Viral Feedback Loop, 200 iterations ...... 30 3.10 Timeseries Comparison ...... 31

4.1 The most-viewed UGC received 45 total hits. Why? ...... 33

vii Chapter 1

Introduction

Before the Web existed, there was simply Internet and, although it primarily consisted of text-based communication, the early Internet nevertheless sustained a vibrant culture. Modern forums and e-mail have much in common with the original text-mediated Internet but, because a sizeable proportion of the world’s population is now online, the impact of these technologies is greater. Social networks have proliferated, connecting friends and families, and facilitating the exchange of ideas (or memes) through sharing and online forwarding. This thesis examines a particular type of content that is commonly shared online in 2012, the image macro (see Figure 1.1 for an actual image macro created by a participant). After providing a historical perspective on image macros, this systematic investigation of participants’ interactions with image macros will explore the process by which content propagates online.

Figure 1.1: A low self-relevance image macro created by a participant with Memelab.

1 Chapter 1. Introduction 2

1.1 Memes, Viral Videos, and Image Macros

The term meme (pronounced like gene) was coined in The Selfish Gene (Dawkins, 1976) to apply the vocabulary of genetics to questions of culture. Although the term is applicable to any sort of cultural object that can be imitated and mutated, the Internet Meme has risen to particular prominence. An early example of an Internet Meme is the “Eternal September” (Fisher, 1994), in which a Usenet (Daniel, Ellis & Truscott, 1980) post to alt.folklore.computers demarcated the Internet’s transition from a relatively small academic community to the exponentially expanding network of modern times. For the sake of clarity, I will use meme in the Dawkins sense, and I will use the term image macro to refer to those pictures with text in them1 With the advent of the World Wide Web (Berners-Lee & Cailliau, 1990) came the introduction and growth of web-based forums, such as the notable online community Something Awful (Kyanka, 1999). The Web was used to propagate one of the first widely-reposted animations, Dancing Baby (Girard et al., 1996), and by the year 2000, the Something Awful community mainstreamed one of the first popularly mutated In- ternet Memes, All Your Base Are Belong To Us (Dibbell, 2008). The proliferation of broadband Internet and peer to peer file sharing (e.g. Napster, Kazaa) enabled increasingly sophisticated video sharing, setting the stage for online video phenomena such as Star Wars Kid (Raza, 2002). By 2006, the BBC estimated Star Wars Kid had been viewed over 900 million times (“Star Wars Kid is top viral video,” 2006), which was accomplished through such rapid online retransmission that its trajectory resembled that of a viral pandemic. Capitalizing on this trend, YouTube launched (Chen, Hurley & Karim, 2005) to become a popular online repositories of viral videos. In 2004, Something Awful community members coined the term image macro, which was named after the mechanism used to insert images into forum posts (‘Image Macro’, 2004). Image macros are characterized as a background picture with one or two lines of text captioning overlaid onto the image, usually for ironic or comedic effect (see Figures 1 and 2). In the same year, a Something Awful community member named “moot” founded 4chan (Poole, 2004), which was an image board (modelled after the popular Japanese forum 2ch) that came to be known for its blanket use of the pseudonym Anonymous and as a prolific incubator of memes. 4chan, in turn, helped launch an early class of image macros known as “LOLcats” (Langton, 2007), which are recognizable as pictures of cats with phonetically or grammatically erroneous captions (e.g. “I can has cheezburger”; see Figure 1.2). LOLcat image macros were collected on a popular blog entitled ican-

1The etymology of image macro is traceable and unambiguous in online communities. Chapter 1. Introduction 3 hascheezburger.com (Nakagawa & Unebasami, 2007), which became so heavily traﬃcked that it was sold to investors within the year for $2 million (Grossman, 2008).

Figure 1.2: The cat that launched an empire: I can has cheezburger?

In 2009, Time Magazine named 4chan’s moot as the year’s most inﬂuential person, even surpassing politicians, celebrities, and criminals for the title (“The World’s Most Inﬂuential Person Is...,” 2009). It was later revealed that the Time poll had been so thoroughly hacked by Anonymous as to manipulate not simply the #1 spot, but also #2-#21, in order to create an acrostic spelling “mARBLECAKE. ALSO, THE GAME” (Schonfeld, 2009), both of which were memes created by 4chan. As a result of stunts like this, the public visibility of memes, and image macros in particular, created demand for simple and user-friendly tools such as quickmeme.com (Wayne, 2010) that enabled novice users with no image-manipulation experience to quickly create image macros. More recently, Cheezburger Inc. raised an additional $30 million from investors to continue the expansion of their commercial image macro/comedy empire (Crunchbase, 2012), while the Canadian magazine Adbusters used a professionally-crafted image macro to launch the Occupy Wall Street movement (Beeston, 2011). Chapter 1. Introduction 4

1.2 Online Behaviour

It has been over 20 years since the first websites came online, and during this time period, significant change has occurred, both online and off. As recently as 2003, e-mail was still considered the “killer app of the Internet,” with more people reporting they engaged in this online activity than any other (Madden & Rainie, 2003). However, by 2009, 78% of teens report going online to play games, whereas only 73% go online for email (Jones & Fox, 2009), signifying a generational shift in the way online resources are incorporated into daily life. It is likely that this trend is influenced by the emergence of social networks as a popular method of connecting with friends and family. Over 400 million users access Facebook on a daily basis (Facebook Usage Statistics, 2012), spending a daily average of 55 minutes (Giampa & Smith, 2011) interacting with a social network that typically consists of 130 others. During this daily routine, over 20% of users report using Facebook to “like” and comment on a friend’s content, while 15% of users update their own status daily (Hampton, Goulet, Rainie & Purcell, 2011). Less information is publicly available about those users who access Facebook infrequently, but there are an additional 400 million users worldwide who fall into this category. As an ever-larger proportion of the world’s population goes online, social processes which were once conducted “in real life” are increasingly mediated through digital chan- nels (Amichai-Hamburger & Vinitzky, 2010). This can be seen as both a blessing and a curse for researchers: although the entirety of online interactions can be observed by monitoring the lines of communication, new methods must be created in order to capture and extract meaningful data by intercepting these interactions. Already, existing research has accomplished a great deal through the judicious application of self-report measures, which can inform this inquiry into the online sharing of image macros. For example, personality is thought to influence social network site usage; Gosling, Augustine, Vazire, Holtzman and Gaddis (2011) report a relationship between extrover- sion and social network site usage, both in terms of usage frequency and level of activity. Other research has found a positive relationship between interpersonal closeness, satisfaction with online communication, and self- disclosure (Pornsakulvanich, Haridakis & Rubin, 2008). This finding is consistent with Zajonc’s theory of Cognitive Tuning (Zajonc, 1960), in which two communicators who agreed on a topic were better able to anticipate their partner’s expectations and needs, resulting in a more appropriately tailored message and more appropriate listening, which facilitated communication. In Zajonc’s model, each communication involves one person who is the transmitter and one who is the receiver, which maps cleanly onto the online world: to share or Chapter 1. Introduction 5 forward is to transmit, and in sharing, the transmitter must tune their message to suit the receiver. Before anyone can forward some content to a partner, how exactly do they go about discovering anything worth forwarding, in the first place? It has been repeatedly hypothesized that Need for Cognition (Cacioppo & Petty, 1982) is linked to online information seeking (Kaynar & Amichai-Hamburger, 2008), which has in turn been linked to the forwarding of online content (Ho & Dempsey, 2010). In the specific case that an image is being forwarded to an online partner (as opposed to a text message, for example), it is worth considering what is already known about this particular medium. For example, Cupchik (1994) observed the role of affective state in creating a context within which an image is interpreted, meaning there is not simply a cognitive component to online image forwarding, but there is also an emotional component. Furthermore, (Leventhal & Cupchik, 1975) noted the influence of affective state upon the interpretation of humorous cartoons, which is particularly relevant in the case of image macros since they are frequently funny or ironic. Amos, Hilscher and Cupchik (2011) observed a correlation between Strong Sense of Self (a Self factor) and Social Exploration (an Internet Motivations factor). Past work has found a relationship between strong sense of self and social exploration, while information seeking has been related to increased forwarding of online content. In the current work, a strong Sense of Self may be related to a Strong Internet Identity, which will in turn lead to increased viral impact due to increased social exploration. These results will extend the previous work by using a behavioural measure (i.e. actual Internet browsing activity) as the dependent measure.

1.3 Viral Feedback Loop

What is the nature of virality2, and how can we predict its occurrence? The consensus media opinion would have us believe it is the content (or even the media itself) that possesses viral characteristics and that we are fatalistically moved (or infected) by it, causing us to spread the viral media to others. No matter how much that notion might appeal to the advertising industry, there is no reason to accept it at face value, so let us entertain an alternative explanation. A casual interrogation of the viral media concept causes the virus metaphor to diverge farther and farther from the idea of inanimate content embodying virality. In so-called “real life,” a biological virus very nearly possesses agency3. At minimum, the virus is a

2a property of or the posession of viral characteristics 3Don’t argue that to a biologist, however, who would argue that the virus is not living at all. Chapter 1. Introduction 6 machine, perhaps a little like a mousetrap, which, once it is wound up, causally springs its trap under certain circumstances. But, who sets the trap? Who manufactured it and who is captured by it? Does the mouse trap itself deserve any of the credit here or is all of the agency invested in the people who cause this thing to be?4

Figure 1.3: Model of Sharing, including the Viral Feedback Loop.

The core theory driving this work is based on a message passing framework, dubbed the Viral Feedback Loop, which is depicted as the thick grey line in Figure 1.3. The Viral Feedback Loop proceeds according to the algorithm depicted in Figure 1.4 which begins when we encounter content (such as a meme) on the Internet. The more times any meme repeats the loop, the more people it has “infected,” and in terms of measurable behaviour, this corresponds with more access requests5 for that particular image macro. 4Well, of course it is not the trap that does anything, because that is how I’ve constructed this argument; the credit goes to the people. 5Each access request is also known as a hit Chapter 1. Introduction 7

1. Receive content (#1b.)

2. React to the content (#2.)

3. If the content is liked . . .

4. . . . then make a prediction about a friend who might like the content (#3.)

5. Share the content (repeat step #1b.)

6. If the prediction was accurate . . .

7. . . . then the friend will also like the content.

Figure 1.4: The Viral Feedback Loop algorithm

1.4 The Present Study

To investigate the Viral Feedback Loop, this work involved the construction of an image macro creation and sharing website called Memelab which is live on the Internet at http://www.meme8.com6. Participants came into the lab, answered a battery of individual difference measures, rated 8 baseline image macros from the stimulus library, and finally each participant made 2 pieces of User-Generated Content (UGC) with Memelab for the purpose of sharing that content with friends. Because meme8.com also functions as an image sharing website7, participants could use a variety of techniques to share the images: email, chat, Facebook, and SMS are all accommodated by the sharing interface. By running the web server, the access patterns that the each image macro received during a 2-month period could be monitored, and from this I was able to count the number of times each image macro was viewed. Background individual difference factor scores capturing dimensions of Self and Mo- tivations for Internet Engagement were calculated, which were then related to the number of times any given image macro was shared. Separately, a model of “liking” was created based on participants ratings of 8 baseline image macros, which was ultimately folded into a computer simulation capable of modelling thousands of participants.

6It is true that meme is not used in the Richard Dawkins sense here, but it had to be used this way because that is what most of the participants would understand. 7That is, in addition to functioning as a content creation tool, meme8.com is also sharing website. Chapter 2

Methods

2.1 Participants

One hundred eighteen (118) undergraduate participants were recruited from introductory psychology courses at a major Canadian university, to whom course credit was provided as compensation. 85% of participants were ﬁrst-year students, and overall the sample was 76% female. 100% of participants were familiar with the Internet, and everybody reported at least some use of the Internet every week. While some participants did report that there were certain days of the week that they did not use the Internet at all, other participants reported that some days they used the Internet for all 24 hours available that day.

2.2 Materials

A background questionnaire was created that collected measures of predictive value. So that there would be a comparable measure of participant responses to memes, a stimulus library was compiled that would be presented to all participants for the purpose of gathering baseline responses from everybody. Finally, the Memelab research platform was created in order to implement a “content creation task,” whereby participants would make something original in the laboratory that could be shared online with friends.

2.2.1 Background Questionnaires

A variety of measures were used or created to capture the variables of interest, which were presented in the background questionnaire.

Self Survey (Amos et al., 2011) assesses participants’ metacognition and self-concept.

8 Chapter 2. Methods 9

Internet Motivations (Amos et al., 2011) assesses participants’ motivations for engaging in online activities.

Internet Use consists of 5 subscales that assess what sorts of devices people use to access online resources, the online communities they participate in, how their Internet use is distributed across the week, their history of posting content online, and their familiarity with memes and image macros.

Creator vs Consumer Scale was created to assess participants’ proclivity towards reading online material, as well as towards creating material for others to read online.

Social Network Questionnaire assesses who the most important people are in the participants’ online network, how close they feel to those people, and which sorts of online tools they use to connect with those people.

Need for Cognition Scale (Cacioppo & Petty, 1982) assesses the motivation to en- gage in complex thought.

Ten Item Personality Inventory (Rammstedt & John, 2007) assesses personality traits.

Mood Scale assesses how the participant feels at the time of the experiment, immedi- ately before rating baseline image macros from the Stimulus Library.

2.2.2 Stimulus Library

The stimulus library consists of 8 image macros which have been downloaded from actual Internet message board postings to http://www.reddit.com and selected according to community popularity as well as content analysis of the image. The library is divided according to subject material, consisting of 4 university-themed and 4 animal-themed images, corresponding to high-student-relevance and low-student-relevance. An example of a high-relevance image from the stimulus library can be seen in Figure 2.11, and a low-relevance image is showin in Figure 2.2. All images were a square or rectangular shape2 having an approximately 4:3 aspect ratio, although the original images were of various resolutions. When these images were

1This particular image macro is an example of the “scumbag pawn star” meme, in which a scummy pawn broker bids much below the real market value of an item. The face is a character from a popular US TV show called “Pawn Stars.” 2Figure 2.2 happens to be the most extreme deviation from a 4:3 aspect ratio, which is to say that the images were “normal” rectangles. Chapter 2. Methods 10

Figure 2.1: Baseline image macro - high relevance

originally downloaded, they were typically compressed using the JPEG image format3. To prepare the images for inclusion in the data collection process, the baseline images were ﬁrst converted to the PNG image format and then scaled to have an approximate width of 800 pixels. This image width was chosen because the resulting images would then be visible without scrolling on the laboratory monitors. The use of PNG, instead of JPEG, ensured that image quality would not suﬀer as a result of any image transformations such as scaling. Figure 2.5 presents an example of a transformed image macro, which is presented along with the survey instrument on the same screen.

The full text of the Baseline Image Macro survey items are presented in Figure 2.3, and were answered using a 7-point Likert scale indiciating level of agreement with each statement, ranging from 1 (disagree strongly) to 7 (agree strongly). These items were presented on the same page as the image itself, and participants could answer the items while looking at the image4.

3JPEG is a so-called “lossy” image compression algorithm, which actually degrades the image every time it is recompressed. This is a librarian’s nightmare. 4although this did occasionally require scrolling Chapter 2. Methods 11

Figure 2.2: Baseline image macro - low relevance

1. This is a high-quality picture

2. I get the point of this picture

3. I am likely to share this image with friends

4. This expresses a concept that is personally meaningful

5. This image is funny

6. If a friend sent this to me, I would be pleased to receive it

7. I would incorporate this into an online conversation

8. I reacted to this image without thinking about it

9. I would like to modify this image and re-share it

Figure 2.3: Baseline Image Macro items Chapter 2. Methods 12

2.2.3 Memelab

Memelab is an online software tool for creating and sharing image macros that was built to enable experimental research into the online sharing processes. Memelab can be simultaneously accessed by multiple users in the lab and across the Internet. This software is hosted by a web server located in the lab that is always locked to provide physical security for the data.

Figure 2.4: Selecting a background image

Memelab presents a graphical user interface (see Figure 2.4) that enables participants to select a background picture, then overlay one or two lines of text captions onto that image (see Figure 2.6 for more detail about this process). It is loosely modelled after existing commercial websites such as memegenerator.net and quickmeme.com which provide similar functionality, but which are not suitable for laboratory use. The Memelab software is written primarily in Python 2.6 with the Bottle framework, Javascript, and HTML 5. The website is hosted on an old lab machine running GNU/Linux (Debian), and served to web clients with nginx 1.0.17 and µwsgi. Memelab provides several key functionalities not present in commercial alternatives, primarily because, as the creator, I have complete control over all aspects of the website. For example, commercial meme websites will syndicate and announce their content, mak- ing it available to search engines for random people to discover. In contrast, meme8.com Chapter 2. Methods 13 explicitly blocked search engines in order to ensure that the participants were the only vector through which to discover the website. Without this experimental control, it would be impossible to assert that all recorded traﬃc was the direct result of sharing, speciﬁcally, and nothing else.

2.3 Procedure

The experiment was broken up into three phases: the Background Survey and Image Macro Baseline, the content creation task, and longitudinal measurement. Typically, the laboratory portion of the experiment would take approximately 45 minutes. Participants would be seated at a cubicle in a quiet room with a computer, keyboard, mouse, and LCD monitor in front of them. After completing the informed consent process, the experimenter explained that most of the experiment would take place on the computer.

Figure 2.5: White-label survey embedded within meme8.com website

2.3.1 Part One: Background and Image Macro Baseline

The background survey was administered through Survey Monkey using an encrypted connection5. In order to create a more convincing experience of using a “live” website,

5Special thanks to the University of Toronto Embodied Social Cognition Laboratory Chapter 2. Methods 14 all Survey Monkey branding was hidden, and instead the surveys were embedded within the meme8.com website (as seen in Figure 2.5). As a result of this camouﬂage, the experiment appeared to participants as if it took place entirely within the website. The Image Macro Baseline was also assessed through Survey Monkey, in the same manner as the Background Survey.

2.3.2 Part Two: Content Creation Task with Memelab

During the content creation task, participants made User-Generated Content (UGC) with Memelab. Participants were asked to think about two friends they listed on their background questionnaire, then to create image macros for the purpose of sharing them with those friends. To accomplish this goal, participants followed a 3-step process on http://meme8.com to create image macros. First, participants selected a background image, then put words onto the image, and ﬁnally they learned about several ways they could share their UGC with friends. The entire process was explained to participants by the experimenter, and a 45-second example video was shown that demonstrated the image macro creation process.

Step One: Select Background

First, the experimenter navigated to the image gallery, where the participant selected a background image from the matrix of available images (see Figure 2.4). The participant selected an image by clicking once on the image that they wanted to share with a friend. Participants could also use the web browser “back” button if they changed their mind and wanted to use a diﬀerent image instead. As with the baseline image macros, the available background images were curated; the images were selected based on their popularity as measured by the quickmeme.com (Wayne, 2010) online community6. The library consists of 12 images, 6 of which are high-relevance7 and 6 of which are low-relevance.

Step Two: Create Caption

Next, participants used the captioning interface to overlay words on top of the image. This minimalistic user interface consists of two copies of the background image: The

6The speciﬁc process I followed was to visit http://www.quickmeme.com on approximately February 3, 2012. Next, I visited the “popular” tab and observed which background images were trending. I then downloaded a selection of the most popular backgrounds (without text) to use as the available background images. 7to undergraduate students Chapter 2. Methods 15

Figure 2.6: Captioning an image macro image on the left includes gray textboxes at the top and bottom that the participant types into, and the image on the right displays a preview of the ﬁnished image macro containing the rendered caption. Participants could change the text caption as many times a they wished by typing in diﬀerent words and clicking the “preview” button repeatedly. Once participants were happy with their content, they clicked “save” and were taken to the sharing interface.

Step Three: Sharing

The sharing interface (as seen in Figure 2.7) was built to display an image macro along with several hints for how to share it. Each image macro is assigned a unique combination of letters that serve as a “key” for ﬁnding that picture again in the future, and each of the sharing methods makes use of this key in some way. Thus, participants learned how to share the image macro using Facebook, by using the URL directly (which is compatible with e-mail, chat, and forums), and by using the QR code8 on their phone. In order to facilitate sharing, the experimenter also wrote the URL for each image macro on a standard take-home form, so participants would have a permanent record of the URLs for the image macros they created. To facilitate this process (and to reduce the possibility of handwriting copying errors), the image macro URLs were made as short as possible; one example is http://meme8.com/alhijv.

8this is the square barcode-esque grid of black squares; these were generated by memelab to contain a URL pointing to the image macro. Chapter 2. Methods 16

Figure 2.7: Sharing an image macro via http://meme8.com

69.171.228.244 - - [24/Apr/2012:19:44:38 -0400] "GET /static/images/memes/fhpsjp.jpg HTTP/1.1" 200 360 32 "-" "facebookexternalhit/1.0 (+http://www.facebook.com/externalhit_uatext.php)"

Figure 2.8: A line from the web server access log

2.3.3 Longitudinal Monitoring

Once participants left the experiment, they were free to do whatever they wished with the URLs they had been given. There was no incentive for them to share (i.e. no raffle or prizes), so any sharing would be completely opt-in and voluntary. Technically speaking, the monitoring stage involved the passive observation of online access requests by Internet users. Because I ran my own web server, I was able to count every time somebody requested to see one of the images. For every such access attempt, a line of text was appended to the web server access log. Figure 2.8 contains an actual entry from the logs. Within that single entry are several features worth noting. In addition to useful information such as the date and time of the access attempt, this entry also identifies the image that was requested (fhpsjp.jpg). Because each image filename is the the same as the Chapter 2. Methods 17

original image macro identifier (in this instance, fhpsjp), it is possible to identify from this log entry which participant ID created the image macro in the first place. Additionally, due to the uniqueness of the identifier, it is also possible to associate features of the image macro with this request (such as the text caption and the friend for whom the image was created). For the purposes of the analyses reported in this thesis, this will be the primary mechanism responsible for the ability to link hits with survey responses and image features. The IP address (in this example: 69.171.228.2449) can be thought of as being similar to a telephone number, and just like a telephone number, the IP address can sometimes convey information such as geographical location (usually the country, and sometimes state or province). This particular log entry demonstrates the peculiar effect of the Face- book Proxy (which identifies itself as the “facebookexternalhit/1.0” useragent), which serves as an intermediary for the very purpose of obfuscating the geographical location of the Facebook user who originated the request10.

2.3.4 Transaction Log Analysis

These raw log entries are not suitable for statistical analysis; at a minimum, these entries do not conform to any data format (e.g. CSV11, tab-delimited) that can be operated upon by statistical software. In addition, certain summary statistics that are not imme- diately manifest in the logs will be most interesting; the most useful datapoint will be “hits,” which can be described as, “for each unique image identiﬁer, how many unique IP addresses requested that image?” In order to derive this statistic, the Python programming language was once again used to construct a log-processing pipeline capable of producing a .CSV ﬁle that could be imported into statistical software for analysis.

2.4 Agent-Based Simulation

Having collected longitudinal sharing data, next a social network simulation was created using the Recursive Porous Agent Simulation Toolkit, also known as Repast (North, Collier & Vos, 2006). An advanced version of Repast has been adapted to the Eclipse Foundation IDE, which provides a friendly method for interacting with the Repast Java libraries via the Groovy programming language. Repast is capable of supporting various

9which happens to be a Facebook privacy proxy 10otherwise, unscrupulous marketers with less noble goals than my own would exploit this information for their own ends. . . and besides, Facebook wants that data for themselves! 11comma-separated values Chapter 2. Methods 18 space topologies, including a graph/network topology that could be considered analogous to an individual’s online social connections. However, a Random Walk (Spitzer, 2001) was instead chosen, in which agents randomly stumble in a direction chosen by chance. The random walk can be thought of as being analogous to the Internet exploration and discovery process because it captures aspects of the unexpectedness with which we sometimes encounter online content.

Figure 2.9: Simulation of Viral Feedback Loop with Repast, 80 iterations

In this model, when two people have randomly walked to the same point in 2- dimensional plane12, they can be thought of as being visitors to the same website, or perhaps that they are chatting online. It is during such encounters that Agents will have the opportunity to share a meme with one another. Figure 2.9 depicts the Experimenter, several hundred simulated participants, and white arrows representing image macros that have been shared within the network. The simulation uses iterations as its notion of time, with one iteration being comparable to a single day in the empirical data. Therefore, since Figure 2.9 depicts iteration 80, it can be thought of as a simulated representation of day 80 in the experiment. The simulation mirrors several elements of the actual experimental procedure. The experimenter13 appears within the simulation as a large Zombie character who recruits Agents to “participate” in the experiment. Once recruited, an Agent will “create” an image macro. The image macro generation process is accomplished by picking 4 values from a normal distribution with the same properties as observed in the sample. Once an Agent has created an image macro, they continue their random walk through space,

12I think of as a courtyard or agora space 13yours truly Chapter 2. Methods 19

def wants_to_share() { def share_score = (0.39 * meme[’funny’]) + (0.45 * meme[’personally_meaningful’]) + (0.045 * meme[’first_person’]) + (0.1 * meme[’content_code’]) return (share_score > 0.5) }

Figure 2.10: An agent decides whether or not to share and each time they bump into another Agent, they might share their image macro (as described in Figure 2.1014) with the other Agent. A rudimentary form of “forgetting” has been built into the agent model such that after a certain number of iterations, an Agent will delete its own image macro. The image macro can therefore continue to propagate through the network via other agents, even though the creator may have forgotten it. Most agents will never be recruited to participate in the experiment, so their primary activity is to “surf the web.” In the simulation, this amounts to a random walk through the agora. Any time an agent in possession of an image macro bumps into another agent, the ﬁrst agent decides whether to share the image macro. This decision was modelled after the empirically-derived model of sharing likelihood, which will be described in the results section.

14The coeﬃcients reported in Figure 2.10 are taken from the results section. Chapter 3

Results

Even though they were provided no incentive to do so, many participants chose to share the content they made in the lab. Figure 3.1 depicts the total number of hits each participant’s UGC received, in which it is observable that a few participants’ image macros received an enormous amount of attention while the majority received almost none.1

Figure 3.1: Frequency of Viral Impact

1The reason every participant has two hit is that participants viewed their image macros once in the lab. Therefore, any image macro receiving more than this amount was viewed outside the lab as well.

20 Chapter 3. Results 21

3.1 Scoring

The background Questionnaire collected items corresponding to several new and estab- lished scales. For each of the following scales, latent constructs were extracted as factor scores. Factor scores of participants’ loadings2 on each factor were calculated using the efa3 procedure in R, then saved in an object for future use (DiStefano, Zhu & Mîndrilă, 2009). Since factor scores are centered at 0, and because they are already standardized, they are already prepared for use in future regressions.

3.1.1 Creator/Consumer Scale

The degree to which people both create and consume content online was assessed through a Creator/Consumer Scale, which was developed based on observations of online beha- viours in several Internet contexts. The full items and their factor loadings are presented in Table 3.1. Participants responded with how much they agreed with each statement using a 7-point likert scale, ranging from 1 (disagree completely) to 7 (agree completely). The scale had good internal reliability, α = 0.82.

Figure 3.2: Creator/Consumer Scale - Eigenvalue scree plot

As shown in the scree plot in Figure 3.2, the elbow is at 2 factors, where the amount explained by any additional factors is seriously reduced. Thus, a 2-factor solution was

2also known as participant sensitivity to a given factor 3To calculate factor scores using efa, use the following: efa(data, . . . , scores=regression) Chapter 3. Results 22

fit to the data using promax rotation because it was assumed that creator/consumer factors would be at least mildly correlated with each other. Items that had a loading of 0.5 or higher were retained for the extraction of factor scores. The fulltext of the items, as well as their factor loadings, are presented in Table 3.1. A majority of the items, each reflecting creation behaviour, loaded strongly onto the Creator factor. Two items reflecting different aspects of online reading loaded onto the Consumer factor.

Factor Item Loading Creator I try to learn new skills related to digital authoring (including 0.58 blogging, photoshopping, HTML, video/audio editing) I create memes and upload them to the internet 0.51 I update an active proﬁle on an online community, forum, or 0.80 blog I have a personal website that I update 0.60 I post comments to blogs or public forums 0.56 I share pictures on my friends’ social network spaces (e.g. Face- 0.60 book wall) I upload my own pictures, videos, writings, or other original 0.57 creations to Internet services Consumer When I read Internet content, I also read the comments about 0.88 that content I return to familiar websites to read content 0.71

Table 3.1: Creator/Consumer factors and item loadings

3.1.2 Internet Motivations

Motivations for engaging the Internet were assessed using items developed in Amos et al. (2011). The full items and their factor loadings are presented in Table 3.2. People responded with how much they agreed with each statement using a 7-point likert scale, ranging from 1 (disagree completely) to 7 (agree completely). The scale had good internal reliability, α = 0.83. As shown in the scree plot in Figure 3.3, the change in eigenvalues between the 4th and 5th is much less than the change between the 3rd and 4th. Thus, a 4-factor solution was ﬁt to the data using promax rotation because it was assumed the factors would be at least mildly correlated with each other. Upon looking at the items that loaded onto Factor 1 in Table 3.2, these seem to reﬂect the idea of being clear about one’s self and being comfortable telling people about it, so Chapter 3. Results 23

Figure 3.3: Motivations for Internet Use - Eigenvalue scree plot Factor Item Loading Strong Internet I contribute to the Internet community at large. 0.59 Identity I am comfortable showing aspects of my everyday life 0.68 on the Internet. I have a lot of pictures of myself on my online profile. 0.63 If I got a new car, I would mention it on my online 0.60 profile. Most of my online profile updates are about myself. 0.63 My online profile gives insight into my identity, mor- 0.51 als, and beliefs. Network Expansion I would add someone I don’t know to my online social 0.53 network. I use the Internet to make new friends. 0.94 Agency/ My use of the computer helps me achieve my goal. 0.67 Effectiveness The Internet allows me to do things I would not be 0.61 able to do otherwise. Without the Internet, I would be less successful. 0.70 Other orientation I use my online profile primarily as a means to advoc- 0.60 ate for the things I believe in. The profile pictures on my online account are the ones 0.51 with the most positive comments and feedback.

Table 3.2: Internet Motivations factors and items loadings Chapter 3. Results 24 this factor has been named Strong Internet Identity. The next factor, which has been called Network Expansion, relates to the way people grow their social networks by adding new people to their circle. The third factor, Agency/Eﬀectiveness, consists of items that are concerned with accomplishing goals. Finally, Other Orientation is composed of two factors that have to do with opinions and attitudes of other people in the participants’ social networks.

3.1.3 Self Factors

The Self Survey was developed in Amos et al. (2011) to capture the Self dimensions that originated from Cupchik (2011). The full items and their factor loadings are presented in Table 3.3. People responded with how much they agreed with each statement using a 7-point likert scale, ranging from 1 (disagree completely) to 7 (agree completely). The scale had questionable internal reliability, α = 0.674.

Figure 3.4: Self Factors - Eigenvalue Scree Plot

As shown in the scree plot in Figure 3.4, the change in eigenvalues starts levelling off at the 5th factor. Thus, a 5-factor solution was fit to the data using promax rotation because it was assumed the factors would be at least mildly correlated with each other. When looking at the items that loaded onto Factor 1 in Table 3.3, these seem to reflect the idea of looking closely at one’s self, so this factor has been named Introspective. The

4This is still well above α = 0.50, which is the cutoﬀ below which internal reliablity is considered “unacceptable.” Chapter 3. Results 25

Factor Item Loading Introspective I question some aspects of myself. 0.90 I am always trying to ﬁgure myself out. 0.87 Strong sense When I interact with others, I put myself in the person’s 0.80 shoes and try to empathize with them. of Self I have a strong sense of who I am. 0.60 Procrastination I tend to procrastinate, leaving things till the last minute 0.77 but would like to learn how to use my time more eﬀect- ively. I want better marks in school but have not made the 0.69 necessary changes in my study habits to achieve this goal. Self-presentation I care a lot about how I present myself to others. 0.57

Table 3.3: Self Survey factors and items loadings

next factor, which has been called Strong Sense of Self, relates to the way think of themselves as distinct from others. The third factor, Procrastination, consists of two cases where a participant is not achieving a particular goal. Finally, Self-presentation is a measure of the degree to which somebody is conscious of how others perceive them. The ﬁfth factor did not have any items loading above 0.5 on it, so it was dropped from the analysis.

3.2 Image Macros: Baseline and User-Generated Con- tent

A very rich source of data was created by participants: User-Generated Content (UGC). The image macros in Figures 3.5 and 1.1 were actually created by participants5. UGC was coded in several ways: (1) the background image was coded as either high-relevance (academic) or low-relevance (animal), and (2) the captions were coded with LIWC to count frequencies of various parts of speech (Pennebaker, Francis & Booth, 2007), including self-reference words like I or myself. As a simple check of the relevance manipulation upon the baseline image macros, a multilevel regression was run to determine how personally meaningful an image macro was given the content category. Even when controlling for ratings of quality, content category is signiﬁcantly predictive of how personally meaningful the image macro is,

5I picked them for presentation here because I liked these ones; I probably would have rated them a 6 or 7 on intention to share. Chapter 3. Results 26 b = 0.489, t(1489) = 11.724, p < 0.001. This conﬁrms that participants viewed academic memes as being more personally meaningful.

Figure 3.5: A high self-relevance image macro created by a participant with Memelab.

When applying the same model to the UGC, participants did not perceive any memes based upon academic backgrounds to be more personally meaningful, b = −0.04, t(113) = −0.794, p = 0.429.

3.3 Model Investigation

3.3.1 Predicting Intention to Share

A repeated measures design was used to collect participant responses to 8 baseline image macros. In order to predict Intention to Share from these measurements, a multilevel model was used to examine responses. Intention to Share the baseline image macros was modelled as a function of content type (academic versus animal), self-reference words in the image macro caption, ratings of how funny the image macro was, and ratings of how personally meaningful the image macro was (as depicted in Figure 3.6). A 2-level multilevel model was used to account for ratings nested within participant by modelling a random intercept for each participant using an unstructured covariance matrix and the Satterthwaite method of estimating degrees of freedom. There was a main effect of perceived funniness on intention to share, b = 0.392, SE = 0.018, t(1483) = 22.213, p < 0.001. There was also a main effect of perceptions of how Personally Meaningful the content was on Intention to Share, b = 0.459, SE = 0.017, t(1483) = 26.279, p < 0.001. There was also a main effect of the number of first-person pronouns on intention to share, b = 0.044, SE = 0.012, t(1483) = 3.558, p < 0.001. There Chapter 3. Results 27

Figure 3.6: Multilevel regression of participants’ likelihood of sharing 8 baseline memes, represented graphically

was also a main eﬀect of content code6 on intention to share, b = 0.098, SE = 0.013, t(1483) = 7.320, p < 0.001.

3.3.2 Predicting Viral Impact

The hypothesized model was tested with path analysis and the estimated model is depicted in Figure 3.7. The model appeared to have good fit, χ2 (9) = 14.272, p = 0.11297, RMSEA = 0, 90% CI [0, 0.144], CFI = 0.91, BIC = 103.06. Having a Strong Internet Identity was significantly predicted by Introspection, β = 0.21, Z = 1.984, p = 0.047, and Strong Sense of Self, β = 0.30, Z = 2.04299, p = 0.041. Having a Strong Internet Identity in turn predicted Viral Impact, β = 0.29, Z = 3.31677, p = 0.001. Predicted Liking was significantly predicted by participants own ratings of how funny their user-generated content was, β = 0.34, Z = 3.645, p < 0.001,

6either Academic or Animal Chapter 3. Results 28

Figure 3.7: A model of web content hits using proximal and distal predictors and although how personally meaningful they thought the image macro was trended towards predictions of liking (β = 0.14), this eﬀect was not reliable, Z = 1.383, p = 0.167. Predicted Liking in turn predicted Viral Impact, β = 0.18, Z = 2.088, p = 0.037.

3.3.3 A Model Incorporating Content

Since several content-related constructs appear in both the Viral Impact and Intention to Share models, I tested a plausible alternative model with Structural Equation Modelling. As shown in figure 3.8, the key difference from the previous model is that this model incorporates objective measures of the content. The hypothesized model was tested with path analysis and the estimated model is depicted in Figure 3.8. The model appeared to have good fit, χ2 (28) = 33.83, p = 0.207, RMSEA = 0.044, 90% CI [0, 0.091], CFI = 0.912, BIC = 160. Thus, a model comparison is relevant. In this model, funny and personally meaningful are viewed as being components of the content, but also objective qualities of the image macro were included in the model, specifically first-person and second-person parts of speech. To maintain consistency with Chapter 3. Results 29

Figure 3.8: Structural Equation Model with a latent construct representing content the initial model, funny and personally meaningful still have a path to predicted liking, which in turn has a path to viral impact. To facilitate identification for the measurement model of Image Macro Content, first-person parts of speech in the content was arbitrarily set to 1. The latent factor of Image Macro Content was not predictive of Viral Impact, β = -0.23, Z = 2.703, p = 0.181. The loading of funny onto content was not significant, β = -0.17, Z = -0.872, p = 0.384. The loading of personally meaningful onto content was also not significant, β = 0.35, Z = 1.400, p = 0.162. The loading of second-person parts of speech onto content was not significant, β = -0.39, Z = -1.65368, p = 0.098. Predicted Liking was significantly predicted by participants own ratings of how funny their user-generated content was, β = 0.35, Z = 3.634, p < 0.001, and although how personally meaningful they thought the image macro was trended towards predictions of liking (β = 0.13), this effect was not reliable, Z = 1.384, p = 0.166. Predicted Liking in turn predicted Viral Impact, β = 0.18, Z = 2.088, p = 0.037. As before, having a Strong Internet Identity was significantly predicted by Introspection, β = 0.19, Z = 1.984, p = 0.047, and Strong Sense of Self, β = 0.19, Z = 2.043, p = 0.041. Having a Strong Internet Identity in turn predicted Viral Impact, β = 0.31, Z = 3.428, p = 0.001.

3.3.4 Model Comparison

Since both models fit the data sufficiently well, a formal model comparison was conducted using the Bayesian Information Criterion (BIC) to see which model was superior. The difference in BIC between the two models was 56.94071 in favour of the simpler model, Chapter 3. Results 30

and considering BIC is a logarithmic scale, this means that, given the data, the more parsimoneous model was nearly 57 times more likely. Therefore, the initial model was retained.

3.4 Simulation

Using various forms of modelling, it was possible to investigate several aspects of the image macro sharing process. However, because the Viral Feedback Loop is an iterative process that involves thousands of diﬀerent people7, the Agent-Based Simulation provides a useful framework for examining a Loop without needing thousands more people.

Figure 3.9: Simulation of Viral Feedback Loop, 200 iterations

The general approach is to base as many simulation parameters as possible upon empirically derived measurements, then run the simulation in an eﬀort to replicate real- world results. Because viral impact is operationalized as hits in the model, it will be expedient to compare the timeseries of hits between the simulation and the real data. The number of agents in the model was estimated at 15,340 based on n = 118 participants posessing an average of 130 social network connections each (Giampa & Smith, 2011). As with the real experiment, 238 image macros were created by participants. As can be seen in the source code for determining whether or not an agent will share an image macro (Figure 2.10), the Agents embody a crude representation of the Inten- tion to Share linear model (Figure 3.6) that was empirically derived. In the timeseries comparison depicted in Figure 3.10, the upper plot presents real data while the lower plot presents simulated data. Although the general shape of the real data was approximated

7at least in the case of the present study Chapter 3. Results 31

Figure 3.10: Timeseries Comparison by the simulation, both the vertical and horizontal scales were distorted. Since it is fairly obvious that these timeseries are diﬀerent, no formal test was attempted.

3.5 Exploratory Analysis

Finally, because social psychological research on memes and social transmission (Berger & Milkman, 2010) is in its infancy, I wanted to follow up with an exploratory stage that would hopefully inform future research. The analyses so far have been testing speciﬁc aspects of the core theory, but it is entirely possible that there is more to this story. In order to ensure that no stone is left unturned, a formal model selection process was conducted using the glmulti package for R with the genetic algorithm set to optimize for BIC. This procedure uses a branching approach to identify the model that simultaneously maximizes model parsemony and the amount of variance in viral impact that is explained by the model. The model selection process converged after 950 iterations, and the parameter estimates are shown in Table 3.4. Chapter 3. Results 32

Estimate Std. Error z value Pr(>|z|) (Intercept) 4.2791 0.5344 8.01 0.0000 scale.content_sum 0.2829 0.0476 5.94 0.0000 scale.std_avg_closeness -0.0851 0.0545 -1.56 0.1185 scale.need_for_cognition -0.0247 0.0053 -4.63 0.0000 scale.tipi.extraversion -0.2459 0.0460 -5.35 0.0000 scale.tipi.conscientiousness 0.1497 0.0450 3.32 0.0009 scale.tipi.openness -0.2743 0.0681 -4.03 0.0001 factor_scores.self.reflectiveness 0.1746 0.0702 2.49 0.0128 factor_scores.self.distinctiveness -0.3218 0.0584 -5.51 0.0000 factor_scores.self.carelessness 0.4489 0.0727 6.18 0.0000 factor_scores.self.aloof -0.3271 0.0944 -3.47 0.0005 factor_scores.state_affect.negative_emotions -0.2965 0.0563 -5.27 0.0000 factor_scores.internet.other_orientation -0.4354 0.0660 -6.60 0.0000 factor_scores.internet.self_disclosure 0.7802 0.0705 11.07 0.0000 factor_scores.internet.agency_effectiveness 0.6846 0.0683 10.03 0.0000 factor_scores.creator_consumer.creator -0.2914 0.0848 -3.44 0.0006 factor_scores.creator_consumer.consumer 0.3653 0.0757 4.83 0.0000 ugc.std_average.friend_is_going_to_like 0.3349 0.0727 4.61 0.0000 ugc.std_average.funny -0.4020 0.0864 -4.65 0.0000 ugc.std_average.i_get_the_point 0.2995 0.0675 4.44 0.0000

Table 3.4: Best Model Selection Chapter 4

Discussion

By creating a web laboratory that convincingly blended into the fabric of online sharing, this work captured a living example of image macro sharing. When reacting to memes as a consumer1, participant intention to share was positively related to features of the content and ratings of the content.

Figure 4.1: The most-viewed UGC received 45 total hits. Why?

When creating new User-Generated Content, Viral Impact was positively related to participants’ predictions of friend liking as well as participants’ Strong Internet Identity. Predictions of how much friends would like UGC were related to ratings of how funny and how personally meaningful the image macro was. Strong Internet Identity was positively related to Introspection and Strong Sense of Self.A Viral Impact model containg ratings of the meme, features of the meme, and individual differences was also found to fit the data, but it was rejected after comparison to the first model.

1i.e. among baseline memes

33 Chapter 4. Discussion 34

4.1 Viral Feedback Loop and Simulation

The Viral Feedback Loop claims that accurate predictions of preferences will increase the rate at which content will be shared, yet this experiment never measured the accuracy of predictions. Even though it is a future direction to directly measure prediction accuracy, the behavioural measure used in the present study (hits) does capture a very meaningful behaviour from online users. Every time one of the meme8.com image macros was forwarded, somebody was implicitly gambling that somebody else would like to see that particular image macro. In an eﬀort to comment on the entire Viral Feedback Loop, this work concludes with an agent-based simulation of image macro sharing. The simulation modelled meme sharing as a function of the linear regression equation for intention to share, which was ultimately able to approximate the shape but not the scale of the observed data. The results are promising enough that more investigation is required.

4.2 Memelab as a Platform

The current research is one application of Memelab. In this case, Memelab was used to create a task that measured Self and Internet Motivation factors then tracked the sharing of UCG into a social network. Another research question that makes use of Memelab might involve US political parties, and yet another might look at persuasion in advertising. In each of these cases, Memelab will be an asset for assessing inputs into the Viral Feedback Loop that will ultimately determine the Viral Impact of any given meme. Memelab is an ecologically valid research tool that loosely mimicks the style of con- temporary image macro generator websites. Participants tended to be web-savvy, so by employing common user-interface idioms, http://meme8.com presented a familiar environment that participants quickly learned to use eﬀectively. These principles generalize to many online scenarios that extend beyond image macro generation and sharing. For example, there are many types of content besides image macros that are currently shared online but that might be interesting to experiment with. Memelab has the potential to be a ﬂexible research tool with applications in many domains.

4.3 College Memes

In February 2012, just as the experiment was starting, an unexpected phenomenon occurred: University-themed memes went viral among North American undergraduates on Chapter 4. Discussion 35

Facebook (Reimold, 2012). As a result, by the time students were recruited for this experiment, not only were participants already familiar with image macros but 32% had already shared an image macro online. Although the baseline image macro task was designed to be as simple as possible, it was nevertheless a little fatiguing. At the end of the baseline, a question asked if participants would be willing to rate more memes and 67% responded that they would, 19% said they would not, and 14% said something else. To put this another way, it genuinely seemed like participants were excited to interact with image macros. These responses also help to explain why participants were willing to share their image macros with friends even though there was no explicit incentive to do so. There is no doubt that the College Memes phenomenon had some impact on this research. If participants were so familiar with image macros, then so were their social network friends. When participants shared their image macros, those memes were re- leased into a community that was already interested in looking at more image macros. The subject of College Memes also dovetailed with the relevance manipulation in this experiment, which might explain why academic backgrounds positively inﬂuenced the viral impact of user-generated content.

4.4 Future Directions

4.4.1 Increased Simulation Realism

An obvious criticism of data simulations is that they do not really represent reality,2 but one response to this criticism is that simulations may always be improved to achieve an ever-closer correspondence with reality. In the case of this simulation, each image macro is reduced to a 4-dimensional vector and this vector is actually a property of each agent. Consider a diﬀerent simulation that represented the image macro as a separate object type, such that the simulation consisted of an Experimenter, Agents, and Image Macros. One consequence of this 3- object simulation is that image macros could be “uploaded” for other agents to discover; the simulation’s mechanism for achieving this would be for agents to “drop” the image macro onto the 2-dimensional agora space for other agents to later stumble across in the course of their random walk. This mechanism would represent the same process as an Internet user who looks at an image macro that has been posted to an Internet message

2It should be pointed out that this is a criticism of Social Psychological laboratory experimentation as well. In fact, some would argue that empiricism is not a representation of reality. Chapter 4. Discussion 36

board. Another direction for future simulation will be to use both Random Walk as well as graph theory to model the ﬂow of memes within a network. In this scenario, Agents would bump into one another locally but still retain long-distance connections with certain other Agents (perhaps dubbed “Friends”). Information could therefore be discovered on one side of the agora space, then rapidly be transmitted to a friend on the other side of the space, where the information would then continue to propagate.

4.4.2 Further Disambiguation of Content and Creator

Because each participant created two image macros, these data contain a repeated measure that can be analyzed using multilevel modelling (MLM). Even though MLM was used to analyze intention to share, there are other research questions where it will be interesting to determine if the eﬀects are driven by the individual or by the content they create. MLM will be particularly interesting for the purpose of extending the Agent-Based Sim- ulation, because as the simulation comes to represent the experiment better, it will be beneﬁcial to separately model the characteristics of the content and the individual.

4.4.3 Conﬁrming the Exploratory Work

When exploratory techniques are used to detect patterns in the data, then the best we can hope for is to create predictions for future replication. The omnibus exploratory test suggests that lots of measured data are predictive of viral impact. It will be very interesting to mine the current data for patterns that could inform future hypotheses.

4.5 Conclusion

The popular belief is that content is viral, but this work presents evidence that the content creator also plays a role in the viral process. Perhaps some people are better creators and sharers, which ultimately has consequences for the viral impact of the content they create. A simulation was developed for the purpose of animating one of the models that was derived from the observed data, and a promising future direction will be to reconcile the diﬀerences between the behaviour of the simulation and the behaviour of participants. The Viral Feedback Loop presents an interesting framework for considera- tion, and Memelab was shown to be a useful tool for investigating this particular viral phenomenon. Bibliography

Amichai-Hamburger, Y. & Vinitzky, G. (2010, November). Social network use and personality. Computers in Human Behavior, 26 (6), 1289–1295. doi:10.1016/j.chb.2010. 03.018 Amos, J., Hilscher, M. C. & Cupchik, G. C. (2011). The self and internet engagement. under review. Beeston, L. (2011, October). The ballerina and the bull. The Link. Retrieved from http: //thelinknewspaper.ca/article/1951 Berger, J. & Milkman, K. (2010). Social transmission, emotion, and the virality of online content. Wharton Research Paper. Retrieved September 15, 2012, from http://cci. som.yale.edu/sites/cci.som.yale.edu/files/BergerVirality_of_Online_Content.pdf Berners-Lee, T. & Cailliau, R. (1990, November). WorldWideWeb: proposal for a Hyper- Text project. CERN. Retrieved from http://www.w3.org/Proposal.html Cacioppo, J. T. & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116–131. Chen, S., Hurley, C. & Karim, J. (2005, February). YouTube. Retrieved from http:// www.youtube.com Cupchik, G. (1994). Emotion in aesthetics: reactive and reflective models. Poetics, 23 (1- 2), 177–188. doi:10.1016/0304-422X(94)00014-W Cupchik, G. (2011). The digitized self in the internet age. Psychology of Aesthetics, Creativity, and the Arts. Daniel, S., Ellis, J. & Truscott, T. (1980). USENET. Retrieved from http://ftp.digital. com/pub/news/a/a.news.tar.Z Dawkins, R. (1976). The selfish gene. New York: Oxford University Press. Dibbell, J. (2008, January). Mutilated furries, flying phalluses: put the blame on griefers, the sociopaths of the virtual world. Wired, 16 (2). Retrieved from http://www. wired.com/gaming/virtualworlds/magazine/16-02/mf_goons DiStefano, C., Zhu, M. & Mîndrilă, D. (2009). Understanding and using factor scores: considerations for the applied researcher. Practical Assessment, Research & Eval-

37 BIBLIOGRAPHY 38

uation, 14 (20), 1–11. Retrieved September 16, 2012, from http://pareonline.net/ pdf/v14n20.pdf Fisher, D. (1994, January). Weeks? hah!! Retrieved from http://groups.google.com/ group/alt.folklore.computers/msg/4bd75d223b992e8d Giampa, A. & Smith, M. (2011, January). How much time does the average user spend on facebook? Retrieved from http://www.quora.com/How-much-time-does-the- average-user-spend-on-Facebook Girard, M., Amkraut, S., Chadwick, J., Bloemink, P., Hutchinson, J. & Felt, A. (1996). Dancing baby. Character Studio. Gosling, S. D., Augustine, A. A., Vazire, S., Holtzman, N. & Gaddis, S. (2011). Mani- festations of personality in online social networks: self-reported facebook-related behaviors and observable profile information. CyberPsychology, Behavior & Social Networking, 14 (9), 483–488. doi:10.1089/cyber.2010.0087 Grossman, L. (2008). The master of memes. Time. Retrieved January 9, 2012, from http://www.time.com/time/magazine/article/0,9171,1821656,00.html Hampton, K., Goulet, L. S., Rainie, L. & Purcell, K. (2011, June). Social networking sites and our lives. Pew Internet & American Life Project. Retrieved from http: //pewinternet.org/Reports/2011/Technology-and-social-networks Ho, J. Y. C. & Dempsey, M. (2010, October). Viral marketing: motivations to forward online content. Journal of Business Research. Special Issue: Internet consumer behavior. Vol 63(9-10), 63, 1000–1006. Jones, S. & Fox, S. (2009, January). Generations online in 2009. Pew Internet & Amer- ican Life Project. Retrieved from http://www.pewinternet.org/Reports/2009/ Generations-Online-in-2009/Generational-Differences-in-Online-Activities.aspx Kaynar, O. & Amichai-Hamburger, Y. (2008, March). The effects of need for cognition on internet use revisited. Computers in Human Behavior, 24 (2), 361–371. doi:10. 1016/j.chb.2007.01.033 Kyanka, R. (1999, November). Something awful. Retrieved from http://www.somethingawful. com Langton, J. (2007, September). Funny how ‘stupid’ site is addictive. The Toronto Star. Retrieved January 9, 2012, from http://www.thestar.com/living/article/257955 Leventhal, H. & Cupchik, G. (1975, July). The informational and facilitative effects of an audience upon expression and the evaluation of humorous stimuli. Journal of Experimental Social Psychology, 11 (4), 363–380. doi:10.1016/0022-1031(75)90016-5 BIBLIOGRAPHY 39

Madden, M. & Rainie, L. (2003, December). America’s online pursuits. Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/Reports/ 2003/Americas-Online-Pursuits.aspx Nakagawa, E. & Unebasami, K. (2007, January). I can has cheezburger. Retrieved from http://www.icanhascheezburger.com North, M. J., Collier, N. T. & Vos, J. R. (2006). Experiences creating three implement- ations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation (TOMACS), 16 (1), 1–25. Retrieved August 29, 2012, from http://dl.acm.org/citation.cfm?id=1122013 Image Macro. (2004, February). SAclopedia. Retrieved January 8, 2011, from http:// forums.somethingawful.com/dictionary.php?act=3&topicid=83 Facebook usage statistics. (2012, January). Facebook. Retrieved from http : / / www . facebook.com/press/info.php?statistics Pennebaker, J. W., Francis, M. E. & Booth, R. J. (2007). LIWC2007: linguistic inquiry and word count. Austin, TX. Retrieved from http://liwc.net Poole, C. (2004, February). 4chan.org. Retrieved from http://www.4chan.org Pornsakulvanich, V., Haridakis, P. & Rubin, A. (2008, September). The inﬂuence of dis- positions and internet motivation on online communication satisfaction and relationship closeness. Computers in Human Behavior, 24 (5), 2292–2310. doi:10.1016/ j.chb.2007.11.003 Rammstedt, B. & John, O. P. (2007, February). Measuring personality in one minute or less: a 10-item short version of the big ﬁve inventory in english and german. Journal of Research in Personality, 41 (1), 203–212. doi:10.1016/j.jrp.2006.02.001 Raza, G. (2002, November). Star wars kid. Reimold, D. (2012, February). College memes madness: students posting non-stop on facebook. http://www.pbs.org/mediashift/2012/02/college-memes-madness-students- posting-non-stop-on-facebook045.html. Retrieved September 15, 2012, from http: //www.pbs.org/mediashift/2012/02/college-memes-madness-students-posting- non-stop-on-facebook045.html Schonfeld, E. (2009, April). 4Chan takes over the time 100. The Washington Post. Re- trieved January 9, 2012, from http://www.washingtonpost.com/wp-dyn/content/ article/2009/04/21/AR2009042101864.html Spitzer, F. (2001). Principles of random walk. Springer Verlag. Wayne. (2010, July). Quickmeme. quickmeme LLC. Retrieved from http://www.quickmeme. com BIBLIOGRAPHY 40

Zajonc, R. (1960). The process of cognitive tuning in communication. The Journal of Abnormal and Social Psychology, 61 (2), 159.