A Quantitative Approach to Understanding Online Antisemitism Part 4: Discussion

Main Take-Aways. To summarize, the main take-away points from our quantitative assessment are:

  1. Racial and ethnic slurs are increasing in popularity on fringe Web communities. This trend is particularly notable for antisemitic language.
  2. 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.
  3. 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.
  4. 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.

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A Quantitative Approach to Understanding Online Antisemitism Part 3: Meme Analysis

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. Continue reading “A Quantitative Approach to Understanding Online Antisemitism Part 3: Meme Analysis”

A Quantitative Approach to Understanding Online Antisemitism Part 2 : Temporal Analysis

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.

Our temporal analysis that shows the use of racial slurs over time on Gab and /pol/, our textbased analysis that leverages word2vec embeddings to understand the use of text with respect to ethnic slurs, and our memetic analysis that focuses on the propagation of the antisemitic Happy Merchant meme. Continue reading “A Quantitative Approach to Understanding Online Antisemitism Part 2 : Temporal Analysis”

A Quantitative Approach to Understanding Online Antisemitism : Part 1 Intro

A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand this worrying trend.

In our latest paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million comments and images from alt-right Web communities like 4chan’s Politically Incorrect board  /pol/) and the Twitter clone, Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the “Unite the Right” rally in Charlottesville. Furthermore, this antisemitism appears in tandem with sharp increases in white ethnic nationalist content on the same communities. Continue reading “A Quantitative Approach to Understanding Online Antisemitism : Part 1 Intro”