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Abstract the Sharing of Internet Memes Is Increasingly Popular Form of Expressing Opinions and Complex Sentiments in an Easily Understood Image

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Abstract the Sharing of Internet Memes Is Increasingly Popular Form of Expressing Opinions and Complex Sentiments in an Easily Understood Image Paper to be presented at DRUID21 Copenhagen Business School, Copenhagen, Denmark October 18-20, 2021 Internet Meme Production and Competition Michael R. Ward University of Texas at Arlington (UTA) Economics [email protected] Abstract The sharing of Internet memes is increasingly popular form of expressing opinions and complex sentiments in an easily understood image. Marketers are exploiting the attention memes generate by enlisting meme influencers to create memes to promote products. I develop a machine learning algorithm that classifies meme images scraped from the meme aggregation sub-Reddit forums to generate a panel of meme posts. The data reveal time-series patterns in meme proliferation and quality, competition for attention across memes, learning-by-doing in meme creation, and potential mechanisms of the learning-by-doing. These findings suggest that the market for meme influencer will tend to be concentrated and may tend to become a superstar market. I. Introduction The popularity of Internet meme sharing through social media has increased dramatically in just a few years. Memes are defined as an element of culture that can be passed on to another individual by nongenetic means, usually imitation. Standardization of Internet memes has emerged as a combination of a picture and a tacit concept linked to the picture. In social media, the image, usually from popular culture, that connotes a specific sentiment on to which the creator has added original text that indicates the sentiment applies in another context. Also, they usually attempt humor. Different Internet meme generator sites (e.g. Imgflip.com, kapwing.com, makeameme.org) have inventories of the more popular meme images with which any user can customize with their own text overlay. However, most of the meme expressions Internet users observe are copies of memes that friends will copy onto online social media. In this way, the most popular expressions of memes can diffuse through a social network quite quickly and broadly. Because Internet memes can command broad user attention, marketers have exploited them as a novel means to promote products (Roache, 2019). Meme influencers have emerged who create, curate, disseminate new memes and new versions of existing memes (Kar, 2020). Mediakix reports that the top 25 Instagram meme influencers in 2020 have a combined nearly 200 million followers.1 Influencers such as @epicfunnypage, @funnymemes, and @ladbible, have millions of followers. Marketers contract with meme influencers to create promotional memes or product placements in memes. Internet memes have been studied in various contexts. Amalia et al (2018) uses image processing and OCR to develop a method of classifying the sentiment in a meme as pro or anti- government. Shabunnia and Pasi (2018) and Weng, et al. (2012) study the identification and 1 https://mediakix.com/blog/instagram-meme-accounts-best-funniest/ (accessed 13 Nov 2020) 1 propagation of textual memes on Twitter. Weng, et al. (2012) find that social network structure and competition for attention explain much of the heterogeneity of meme popularity and persistence Xie, et al. (2011) study the virality and influence of memes embedded in videos about newsworthy events. In a series of papers, Coscia (2013, 2014, and 2018) examines how meme image similarity, faddishness, and competition affect the success of a meme. Since these studies also study stock images with idiosyncratic text, they are most closely related this research. Research on Internet memes has largely focused the determinants of meme success in becoming viral in social networks. Spitzberg (2014) synthesizes multiple theories into a model of meme diffusion and Gleeson, et al. (2014) show how competition between memes for attention generates popularity measures that can be described by a critical branching process. This predicts power-law distribution of popularity with heavy tails. He et al (2016) overcome impractical assumptions and difficulty characterizing dynamic information to develop a model-free scheme to rank meme popularity in online social media and the social network. The model of He, et al. (2019) uses meme attributes to evaluate user behavior and dynamic network structure. Xie, et al. (2011) construct a graph model connecting people and content that allowed the detection of certain regularities. For example, content that will eventually have a long lifespan can often be predicted within a day. Weng, et al. (2012) model competition for attention and claim that combination of social network structure and competition is sufficient to predict broad diversity in meme popularity, lifetime, and user activity without reference to exogenous factors such as intrinsic meme appeal, user influence, or external events. Bonchi, et al. (2013) develop heuristics for a platform selecting among memes to promote maximum virality. Guadadno, et al. (2013) show that a stronger emotional response to a meme increases its propensity to go viral. 2 These analyses typically compare one meme against another. The focus of this study is to examine within-meme fluctuations in a meme’s usage. I collected a sample of meme related posts from Reddit sub-forums dedicated to meme culture. A machine learning algorithm was developed to match the image of a meme post to popular meme images. Matches are never perfect because the text always differs across posts. However, the algorithm correctly classified nearly 70,000 posts into 533 existing meme templates resulting in a separate time series for a number of cross-sections of memes that includes information on the number of posts, their authorship, their popularity score, and the number of comments on the posts. In keeping with Coscia (2013, 2014, and 2018), the common underlying image will be referred to as an Internet meme while the various implementations of the meme, in this case a particular post to Reddit, will be referred to as a meme expression. I develop panel estimators to study the evolution of memes over time. The estimator identifies causal effects from a difference-in-difference by including two-way fixed effects model (by meme and time period). The fixed effects for individual memes will capture the time invariant differences in popularity across memes that had been the focus of much of the prior research. The fixed effects for time periods will capture the overall growth in the phenomenon of meme sharing. Thus, identification is driven by time variation in within The empirical analysis generates multiple insights. First, the proliferation of a meme is self-reinforcing with more and higher quality posts generating more future posts. Second, this proliferation tends to temporarily reduce the perceived quality each future post using the meme. Third, there is dynamic competition across memes for user attention. Fourth, there is learning- by-doing in meme creation. Fifth, this learning-by-doing by meme creators may be meme- 3 specific and does not result from learning to exploiting timing patterns that could be used to predict when a post is most likely to succeed. These findings have implications for the meme influencer market. While there are low entry barriers, and meme influencers provide differentiated content to different market segments, learning-by-doing would tend concentrated the market structure. Perhaps a better analogy would by a superstar market with a few providers out of many dominating the page views. Since meme copying enhances the value of its creator, the market should not be affected by intellectual property concerns. These features suggest that meme usage will thrive even as it evolves. II. Testable Implications The appeal of different meme images differs across individuals. Some are shared more than others and are “up voted” more often. This cross-sectional variation represents differences in demand. However, most of the within meme variation in the number of meme expressions and how well they are received will be determined by time-varying supply side factors. A meme poster with a topical message will select the meme image best suited to represent that image. The poster will tend toward meme images that have recently been especially well received. In this “market,” supply and demand are equilibrated at price of zero; there are no pecuniary costs. While the pecuniary price of a meme is zero, there are time costs in both the consumption and creation of a meme. For consumption, the time may only be a few seconds to read the post, and perhaps to rate it, comment on it, or copy and paste it into one’s social media. Higher quality memes will induce more audience members to bear these costs. Differences in the score that a post achieves through “up votes” represents differences in the number of meme consumers who value the post enough to bear that time cost. The time costs to create a post from an existing 4 meme range from a few minutes to an hour. The steps may include finding the meme image online, adapting the appropriate text for the current application, and posting it on the Internet to a site such as Reddit. Supply might shift due to the difficulty of any of these tasks. But any variation in difficulty will affect all meme creation and will not be meme specific as studied here. Meme Popularity A post of a meme that is particularly compelling could affect the future use of the meme. Such a post may have touched on a nuance of the meme that had not been appreciated previously. If so, the meme will be rated highly by a larger audience. Moreover, that nuance may be expanded upon in future incarnations of the meme. This effect may be observed in two ways. Meme creators will be enticed by an anticipated increase in demand to create more posts using a meme whose recent posts are perceived to be better. Additionally, more meme creators will adopt the now more popular meme as part of their repertoire.
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