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

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

URL: http://arxiv.org/abs/2101.06535v1
Date: Sat, 16 Jan 2021 22:36:51 GMT
Title: Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes
Authors: Chen Ling, Ihab AbuHilal, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, and Gianluca Stringhini
Abstract summary: We find that highly viral memes are more likely to use a close-up scale, contain characters, and include positive or negative emotions. On the other hand, image memes that do not present a clear subject the viewer can focus attention on, or that include long text are not likely to be re-shared by users. We train machine learning models to distinguish between image memes that are likely to go viral and those that are unlikely to be re-shared.
Score: 11.491215688828518
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Despite the increasingly important role played by image memes, we do not yet have a solid understanding of the elements that might make a meme go viral on social media. In this paper, we investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-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 set of 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 a close-up scale, contain characters, and include positive or negative emotions. On the other hand, image memes that do not present a clear subject the viewer can focus attention on, or that include long text are not likely to be re-shared by users. We train machine learning models to distinguish between image memes that are likely to go viral and those that are unlikely to be re-shared, obtaining an AUC of 0.866 on our dataset. We also show that the indicators of virality identified by our model can help characterize the most viral memes posted on mainstream online social networks too, as our classifiers are able to predict 19 out of the 20 most popular image memes posted on Twitter and Reddit between 2016 and 2018. Overall, our analysis sheds light on what indicators characterize viral and non-viral visual content online, and set the basis for developing better techniques to create or moderate content that is more likely to catch the viewer's attention.

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