Main Take-Aways. To summarize, the main take-away points from our quantitative assessment are:
- Racial and ethnic slurs are increasing in popularity on fringe Web communities. This trend is particularly notable for antisemitic language.
- Our word2vec models in conjunction with graph visualization techniques, demonstrate an explosion in diversity of coded language for racial slurs used in /pol/ and Gab. Our methods demonstrate a means to dissect this language and decode racial discourse on fringe networks.
- The use of ethnic and antisemitic terms on Web communities is substantially influenced by real-world events. For instance, our analysis shows a substantial increase in the use of ethnic slurs including the term “jew” around Donald Trump’s Inauguration, while the same applies for the term “white” and the Charlottesville rally.
- When it comes to the use of antisemitic memes, we find that /pol/ consistently shares the Happy Merchant Meme, while for Gab we observe an increase in the use in 2017, especially after the Charlottesville rally. Finally, our influence estimation analysis reveals that /pol/ is the most influential actor in the overall spread of the Happy Merchant Memes to other communities in our sample, possibly due to the large volume of Happy merchant memes that are shared within the platform. The Donald however, is the most efficient actor in pushing Happy Merchant memes to all the other sampled Web communities.
Antisemitsm has been a historical harbinger of ethnic strife. While organizations have been tackling antisemitism and its associated societal issues for decades, the rise and ubiquitous nature of the Web has raised new concerns. Antisemitism and hate have grown and proliferated rapidly online, and have done so mostly unchecked. This is due, in large part, to the scale and speed of the online world, and calls for new techniques to better understand and combat this worrying behavior. In this paper, we take the first step towards establishing a large-scale, scientifically grounded, quantitative understanding of antisemitism online. We analyze over 100M posts from July, 2016 to January, 2018 from two of the largest fringe communities on the Web: 4chan’s Politically Incorrect board (/pol/) and Gab (a Twitter-esque service). We find evidence of increasing antisemitism and the use of racially charged language, in large part correlating with real-world political events like the 2016 US Presidential Election. We then analyze the context this language is used in via word2vec, and discover several distinct facets of antisemitic language, ranging from slurs to conspiracy theories grounded in biblical literature. Finally, we examine the prevalence and propagation of the antisemitic “Happy Merchant” meme, finding that 4chan’s /pol/ and Reddit’s The Donald are the most influential and efficient, respectively, in spreading this antisemitic meme across the Web. We are certainly not the first to study antisemitism online. However, our approach differs substantially from the one traditionally taken by organizations like the Anti-Defamation League in several important ways. First, we eschew the use of surveys and qualitative analysis in favor of large-scale, datadriven, reproducible measurement. Second, our work builds upon the scientific literature resulting in well understood and open methodology. Third, the toolkit we present provides a clear direction for building automated, scalable, real-time systems to track and understand antisemitism and how it evolves over time.
That said, our work is not without limitations. First, most of our results should be considered a lower bound on the use of antisemitic language and imagery. In particular, we note that our quantification of the use of the “Happy Merchant” meme is extremely conservative. The meme processing pipeline we use is tuned in such a way that many Happy Merchant variants are clustered along with their “parent” meme. Second, our quantification of the growth antisemitic language is focused on two particular keywords, although we also show how new rhetoric is discoverable. Third, we focus primarily on two specific fringe communities. As a new community, Gab in particular is still rapidly evolving, and so treating it as a stable community (e.g., Hawkes processes), may cause us to underestimate its influence. Regardless, there are several important recommendations we can draw from our results. First, organizations such ashe ADL and SPLC should refocus their efforts towards open, data-driven methods. Small-scale, qualitative understanding is still incredibly important, especially with regard to understanding offline behavior. However, resources must be devoted to scientifically valid large-scale data analysis. More importantly, there is a need for greater transparency both in data (and its collection process) and the methods used for analysis. The scale of the problem of online hate has surpassed the ability of a single organization to solve on its own. Instead, we argue that traditional anti-hate organizations should form more intimate relationships with scientists, not just allowing, but encouraging peer-reviewed and open contributions to the scientific literature, in addition to their traditional modus operandi of public education.
Second, we believe that–regardless of the participation of anti-hate organizations–scientists, and particularly computer scientists, must expend effort at understanding, measuring, and combating online antisemitism and online hate in general. The Web has changed the world in ways that were unimaginable even ten years ago. The world has shrunk, and the Information Age is in full effect. Unfortunately, many of the innovations that make the world what it is today were created with little thought to their negative consequences.
For a long time, technology innovators have not considered potential negative impacts of the services they create, in some ways abdicating their responsibility to society. This work provides solid quantified evidence that the technology that has had incredibly positive results for society is being co-opted by actors that have harnessed it in worrying ways, using the same concepts of scale, speed, and network effects to greatly expand their influence and effects on the rest of the Web and the world at large.