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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.
© 2018 ACM 0001-0782/18/1 $15.00
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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