  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.
