Communications of the ACM

We used the frequent-itemset-mining algorithm Eclat4 to extract the frequent n-grams and discard an n-gram if it has not been used for a certain number of days. We also discarded all n-grams that are proper subsets of another n-gram.

We performed the analysis using different thresholds to ensure independence between parameter choice and results. From most- to least-restrictive threshold choices, we obtained 2,731 to 5,585 protomemes on Reddit and 817 to 2,538 protomemes on Hacker News; the preprocessed data we used is available for result replication.b

Results

We now first show how popularity spikes result in reduced future popularity for a meme, then introduce the concept of “canonicity” and how it can shed light on this phenomenon.

Popularity curse. Common sense tells us that popular ideas are likely to be imitated; a protomeme used in a very popular post today will be used in many posts tomorrow. Such intuition about the demand-supply relation on the web is corroborated by several studies, including Ciampaglia et al. 9 However, dissimilarity-driven success theory would predict that flooding a system with imitations of a protomeme will cause the imitations to be less popular. At first glance, such a prediction might seem to find support in two observations: the average score of the posts containing a protomeme is less than expected the day after it experiences a popularity spike (see Figure 1a and Figure 1c), and the number of posts containing that protomeme increases (see Figure 1b and Figure 1d).

These observations support our theory about viral connections but do not prove it. First, the total score awarded and the average score per post are not constant over time (see the online appendix, dl.acm.org/cita- tion.cfm?doid=3158227&picked=for mats). The lower score might be just a relative change; if, for example, there are fewer upvotes awarded on that particular day, a lower absolute number could still represent an increase in upvote share for the day.

Second, each protomeme is characterized by its own expected populari- b http://www.michelecoscia.com/?page_id=870

highly visible forever. The most popular (highest upvote/downvote ratio) posts appear on Reddit’s “front page,” giving them a further boost in visibility. By default, the front page hosts 25 posts. Each entry in the dataset we studied consisted of a post, its title, and its number of upvotes/downvotes that were combined in a post score by Reddit’s sorting algorithm. Note our research can observe only the final score of a post, not its full upvote timeline. This might introduce bias when looking to establish whether or not the post hit the front page. We assume the final post score is highly correlated with the post score on its first day of life. We base this assumption on the fact that the vast majority of upvotes come within 24 hours following post submission.

Note the terms “score” and “ popularity” are not interchangeable, as they refer to related but different concepts. Score is the one-off measurement of a single instance of a protomeme in a day, and popularity is the overall success of all instances of all memes over a longer period of time.

All 22,329,506 posts added to Reddit from April 5, 2012 to April 26, 2013 were part of the dataset. To cross-test our results, we also used a similar dataset from Hacker News, which uses the same dynamics as Reddit though focuses on a more specialized technical audience and has a much smaller user base. The Hacker News dataset included 1,194,436 posts from January 7, 2010 to May 29, 2014.

Here, we consider protomemes, or a catchphrase with the potential for going viral. Note there are more possible types of memes (such as pictures and videos), but given the nature of the data we limit ourselves here to catchphrases. Catchphrases were also used as meme proxy in Memetracker18 and Nifty. 22 We extracted them using information taken exclusively from the post title.

We apply our definition by borrowing the bag-of-words methodology from the text-mining literature, meaning a protomeme is seen as a set of at least two “tokens” (also called an “ n-gram” with n ≥ 2). A token is a word that is stemmed whereby “stop words” are filtered out and are not tokens. To be classified as a protomeme, an n-gram must have been used frequently and constantly over the observation period.