are able to overcome the popularity curse predicted by the theory.

 

The Canon Effect

Following the argument that success is associated with dissimilarity, we now hypothesize that a post including a widely used protomeme m the day after m hit the front page can still be successful if it is dissimilar from all other posts using m. In this way, the post is able to attract most of the attention network users are directing toward protomeme m.

To test this claim we first need a measure for the uniqueness of a post. In Coscia, 11 we proposed a meme similarity measure that cannot be used here because it calculates meme-meme similarity, while here we consider only post-post similarity within the same protomeme. Moreover, our earlier11 measure applies only to a subtype of the memes shared on Reddit. We cannot use the measure developed by Lakkaraju et al. 17 because it measures the similarity of a post to the subcommunity it is shared in, ignoring the meme it implements. Here, we focus on Reddit and expect a null result for Hacker News, as we showed it to be less prone to the fad effect.

cess theory. Higher l values decrease the estimated effect, because we included lower-ranked posts that might not actually have hit the front page (all p-values are significant, with p < 0.001). Focusing on Reddit (see Figure 2a) values fall within the ] −0.1 : −0.02[ interval. A value of −0.1 implies a score-reduction factor equal to e−0.1, which is close to 10%. This means that ranking in the top 25 posts on a particular day reduces the next day’s median score of a protomeme by almost 10%. The effect in Hacker News appears to be even stronger.

We obtained these results with fixed frequency thresholds, using Table 1 and Table 2 to show the robustness of the results with different threshold choices. In them, we fix l = 25 for Reddit and l = 30 for Hacker News. For all threshold choices in Reddit and for most threshold choices in Hacker News, the results were consistently negative and significant with our main result: hitting the front page results in lower expected popularity on the following day.

One could object to these results using the regression-toward-the-mean argument; that is, once the very visible front-page protomeme instance is copied many times, each copy tends to score approximately the protomeme’s average, which is lower than the spike. But the regression already corrects for this using the protomeme random effects. If the argument would be true, β1 would equal 0. In fact, the average is 0 calculated over 50 null models, where protomeme scores are generated randomly, preserving each protomeme’s average score and standard deviation (see Figure 2a and Figure 2b), disproving the regression-toward-the-mean argument.

To summarize, after hitting the front page, a protomeme will likely be used more frequently—15% more in Reddit, 8% more in Hacker News, as reported in Figure 1b, and Figure 1d. β1 values in the model suggest that posts including this protomeme will likely have a lower score (10% lower in Reddit, 23% lower in Hacker News), confirming the expectation. The effect is significant and independent of the recent overall history of the protomeme, changes in average post score, and front page size.

So, if hitting the front page is bad for
subsequent protomeme posts, why
does common sense tell us the oppo-
site? We propose that a protomeme ap-
pearing on the front page two days in
a row is very noticeable, and we just
do not realize that, on average, the
protomeme is doing poorly. We run
the same regression, changing the
target variable to the maximum
score (MAX Model) instead of the
median. In this model, for Reddit,
the sign β1 is the opposite of the β1
sign for the MED Model (see the on-
line appendix). If protomeme m hits
the front page on day i − 1, the top-
scoring post containing protomeme
m on day i improves. This does not
happen for Hacker News, and our hy-
pothesis is that Hacker News is more
resilient to fads, as it is used mostly for
professional purposes, rather than hu-
mor, as with Reddit.

Hitting the front page is thus associated with a larger number of subsequent posts including protomeme m, which by itself is associated with less expected popularity for the same posts. However, in some scenarios, the best-ranked posts containing protomeme m can still hit the front page more easily than usual. We now turn our attention to this subset of special posts, explaining why they

Table 1. Effect on β1 of different threshold options for the Reddit dataset. Each row is a different frequency threshold, or minimum share of posts that must include the protomeme, or 0.004 = 0.4% of posts. Each column is the minimum share of days in which at least one post including the protomeme appeared, or 0.91 = 91% of days. Significant values with p < 0.01 are in bold. The threshold values included in Figure 2a are in italic.

sup-end 0.91 0.92 0.93 0.94 0.95 0.0045 –0.095 –0.087 –0.101 –0.075 –0.039 0.0045 –0.095 –0.087 –0.100 –0.075 –0.039 0.005 –0.109 –0.098 –0.104 –0.080 –0.046 0.0055 –0.093 –0.087 –0.079 –0.071 –0.038 0.006 –0.064 –0.056 –0.055 –0.061 –0.043

Table 2. Effect on β1 of different threshold choices for the Hacker News dataset. Rows and columns are interpreted, as in Table 1, and significant values with p < 0.01 are highlighted in bold. The threshold values in Figure 2b are in italic. We chose the different threshold values to accommodate the significant difference in size between the two datasets.

sup-end 0.02 0.03 0.04 0.0433 0.0466 0.0055 -0.125 -0.099 -0.271 -0.138 -0.294 0.006 -0.173 -0.152 -0.284 -0.138 -0.294 0.0065 -0.214 -0.199 -0.284 -0.135 -0.294 0.007 -0.129 -0.109 -0.129 -0.101 -0.239 0.0075 0.007 0.019 0.007 0.009 -0.132