Abstract
Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other.
Highlights
Over the past decade, Internet memes have become a pervasive phenomenon in contemporary Web culture (Laineste and Voolaid 2017)
We study what incremental predictive power image related attributes have over textual attributes on meme popularity
We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with Area under the curve (AUC)=0.68
Summary
Internet memes have become a pervasive phenomenon in contemporary Web culture (Laineste and Voolaid 2017). Due to their popularity, memes have received considerable attention in areas such as pop culture, marketing, sociology, and computer science (Bauckhage et al 2013; Journell and Clark 2019). Web culture is moving faster than ever and social media sites have exploded with coronavirus memes as people all over the world try to take this serious situation with a pinch of humor (Bischetti et al 2020). Memes are a source of humor and draw attention to poignant cultural and political themes (Brodie 2009). Many authors have explored the social network factors that lead
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