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Despite the increasingly important role played by image memes, we do not yethave a solid understanding of the elements that might make a meme go viral onsocial media. In this paper, we investigate what visual elements distinguishimage memes that are highly viral on social media from those that do not getre-shared, across three dimensions: composition, subjects, and target audience.Drawing from research in art theory, psychology, marketing, and neuroscience,we develop a codebook to characterize image memes, and use it to annotate a setof 100 image memes collected from 4chan's Politically Incorrect Board (/pol/).On the one hand, we find that highly viral memes are more likely to use aclose-up scale, contain characters, and include positive or negative emotions.On the other hand, image memes that do not present a clear subject the viewercan focus attention on, or that include long text are not likely to bere-shared by users.  We train machine learning models to distinguish between image memes that arelikely to go viral and those that are unlikely to be re-shared, obtaining anAUC of 0.866 on our dataset. We also show that the indicators of viralityidentified by our model can help characterize the most viral memes posted onmainstream online social networks too, as our classifiers are able to predict19 out of the 20 most popular image memes posted on Twitter and Reddit between2016 and 2018. Overall, our analysis sheds light on what indicatorscharacterize viral and non-viral visual content online, and set the basis fordeveloping better techniques to create or moderate content that is more likelyto catch the viewer's attention.

Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes