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Michele Coscia ( michele_coscia@hks.harvard.edu) is a research fellow in the Center for International Development at Harvard University, Cambridge, MA.

The author thanks Clara Vandeweerdt, Frank Neffke, and Andres Gomez for useful discussions on the statistical methodology. This research is partly supported by the Walloon National Fund for Scientific Research (FNRS), grant #24927961.

and gene dynamics. Most mutations are harmful or irrelevant, while also lowering an organism’s fitness. In protomemes, if the change is not judged “suitable” for the protomeme by the user community, it will be selected against and gradually lose relevance. This is one of many possibilities and must be properly tested before it can be considered suitable. We leave such a test for future work. However, this result could explain why meme content is not a promising predictor of meme popularity. 6 Since changing meme content can go both ways, increasing or decreasing meme fitness, the effects might cancel out.

Conclusion

We have tested some of the predictions of a theory that claims that meme success eschews similarity, because similar memes interfere with one another and get less attention. 11 We tested the theory on Reddit and Hacker News, two popular social-bookmarking websites. Successful posts can hit each site’s highly visible front page and then be copied many times over by people who want to use them to be able to get their own posts to appear on the front page. The expected popularity of these posts should thus decrease. We showed that this is the case, though on Reddit some posts might still experience subsequent popularity spikes; Hacker News appears to be resilient to this phenomenon. We explain this apparent contradiction by showing these posts (with persistent popularity spikes on Reddit) have low canonicity; that is, they are usually dissimilar from the average post containing their protomeme. We showed that canonicity has a non-linear effect.

These results open the way to future
work. First, computational social scien-
tists can now move the theory closer to
practice, performing, say, a controlled
experiment where they select front-page
memes from Reddit and semiautomati-
cally generate imitating posts with vary-
ing degrees of canonicity. By releasing
the posts on Reddit, they should ob-
serve in which cases low-canonicity
posts tend to garner more upvotes and
in which cases high canonicity is help-
ful. And second, the theory makes
claims that are not in line with another
theory of meme popularity—the one
giving factors other than meme content
greater weight in predicting its success.
In Cheng et al., 6 Gleeson et al., 15 and
Weng et al., 23 meme content and struc-
ture were found to be a weaker explana-
tory factor for meme popularity. Better
predictors are meme timing and the
social network position of the meme
creators. We thus recommend reunit-
ing the two theories in a unified meme-
analysis framework.

Finally, computational social scientists could extend the investigation of memes by studying the effect of negative votes: we expect it will show non-trivial dynamics; a vote, even if negative, still comes from a person paying attention to the concept, though its effect is to prevent other people from seeing it. This information was not available at the time of our study, but Reddit started to provide it in 2017.

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