Disclaimer. Note that content posted on both Web communities can be characterized as highly offensive and racist. In this post, we discuss our analysis without censoring any offensive language, hence we inform the reader that this post contains language that is likely to be upsetting.
In addition to hateful terms, memes also play a well documented role in the spread of propaganda and ethnic hate in Web communities. To detail how memes spread and how different Web communities influence one another with memes, our previous research established a pipeline which automatically collects, annotates, and analyzes over 160M memes from over 2.6B posts from from Web communities; Reddit, /pol/, Gab, and Twitter. Within Reddit, we pay particular attention to The Donald subreddit (The Donald), a Trump supporting subreddit which notoriously propagates hateful memes and propaganda. In a nutshell, we use perceptual hashing and clustering techniques to track and analyze the propagation of memes across multiple Web communities. To achieve this, we rely on images obtained from the Know Your Meme (KYM) site, which is a comprehensive encyclopedia of memes. In this work, we use this pipeline to study how antisemitic memes spread within and between these Web communities, and examine which communities are the most influential in their spread. To do this, we additionally examine two mainstream Web communities, Twitter and Reddit, and compare their influence (with respect to memes) with /pol/ and Gab. Specifically, we focus on the Happy Merchant meme illustrated in Fig. 1, which is an especially important hate-meme to study in this regard for several reasons. First, it represents an unambiguous instance of antisemitic hate, and second, it is extremely popular and diverse in fringe Web communities like /pol/ and Gab.
First, we aim to assess the popularity and increase of use over time of the Happy Merchant meme on /pol/ and Gab. Fig. 7 shows the number of posts that contain images with the Happy Merchant meme for every day of our /pol/ and Gab dataset. 4 We further note that the numbers here represent a lower bound on the number of Happy Merchant postings: our image processing pipeline is conservative and only labels clusters that are unambiguously Happy Merchant; variations of other memes that incorporate the Happy Merchant are harder to assess. We observe that /pol/ consistently shares antisemitic memes over time, whereas on Gab we note a substantial and sudden increase in posts containing Happy Merchant memes immediately after the Charlottesville rally. Our findings on Gab dramatically illustrate the implication that real world eruptions of antisemitic behavior can catalyze the acceptability and popularity of antisemitic memes on other Web communities. Taken together, these findings highlight that both communities are exploited by users to disseminate racist content that is targeted towards the Jewish community.
Another important step in examining the Happy Merchant meme is to explore how clusters of similar Happy Merchant memes relate to other meme clusters in our dataset. One possibility is that Happy Merchants make-up a unique family of memes, which would suggest that they segregate in form and shape from other memes. Given that many memes evolve from one another, a second possibility is that Happy Merchants “infect” other common memes. This could serve, for instance, to make antisemitism more accessible and common. To this end, we visualize in Fig. 8 a subset of the meme clusters, which we annotate using our KYM dataset, and a Happy Merchant version of each meme. This demonstrates numerous instances of the Happy Merchant infecting well-known and popular memes.
Influence Estimation.
While the growth and diversity of the Happy Merchant within fringe Web communities is a cause of significant concern, a critical question remains: How do we chart the influence of Web communities on one another in spreading the Happy Merchant? We have, until this point, examined the expanse of antisemitism on individual, fringe Web communities. Memes however, develop with the purpose to replicate and spread between different Web communities. To examine the influence of meme spread between Web communities, we employ Hawkes processes, which can be exploited to measure the predicted, reciprocal influence that various Web communities have to each other. We fit Hawkes models for all of our annotated clusters and report the influence in two ways as in. First, we report the percentage of events expected to be attributable from a source community to a destination community in Fig. 9. In other words, this shows the percentage of memes posted on one community which, in the context of our model, are expected to occur in direct response to posts in the source community. We can thus interpret this percentage in terms of the relative influence of meme postings one network on another. We also report influence in terms of efficacy by normalizing the influence that each source community has, relative to the total number of memes they post (Fig. 10). We compare the influence that Web communities exert on one another for the Jewish-related Happy Merchant memes (HM) and all other memes (OM) in the graph. To assess the statistical significance of the results, we perform two-sample Kolmogorov-Smirnov tests that compare the distributions of influence from the Happy Merchant and other memes; an asterisk within a cell denotes that the distributions of influence between the source and destination platform have statistically significant differences (p < 0.01). Our results show that /pol/ is the single most influential community for the spread of memes to all other Web communities. Interestingly, the influence that /pol/ exhibits in the spread of the Happy Merchant surpasses its influence in the spread of other memes. However, although /pol/’s overall influence is higher on these networks, its per-meme efficacy for the spread of antisemitic memes tended to be lower relative to non-antisemitic memes with one intriguing exception of The Donald. Another interesting feature we observe about this trend is that memes on /pol/ itself show little influence from other Web communities; both in terms of memes generally, and non-antisemitic memes in particular. This suggests a unidirectional meme flow and influence from /pol/ and furthermore, suggest that /pol/ acts as a primary reservoir to incubate and transmit antisemitism to downstream Web communities.