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There is a lot of antisemitic hate speech on social media—and algorithms are partly to blame

Hatred against Jews on social media is often expressed in stereotypical depictions of Jews that stem from Nazi propaganda or in denial of the Holocaust.

There is a lot of antisemitic hate speech on social media—and algorithms are partly to blame
[Source photos: Walkator/Unsplash; Anete Lusina/Unsplash]

Antisemitic incidents have shown a sharp rise in the United States. The Anti-Defamation League, a New York-based Jewish civil rights group that has been tracking cases since 1979, found that there were 2,717 incidents in 2021. This represents an increase of 34% over 2020. In Europe, the European Commission found a sevenfold increase in antisemitic postings across French language accounts, and an over thirteenfold increase in antisemitic comments within German channels during the pandemic.

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Together with other scholars who study antisemitism, we started to look at how technology and the business model of the social media platforms were driving antisemitism. A 2022 book that we co-edited, Antisemitism on Social Media, offers perspectives from the U.S., Germany, Denmark, Israel, India, U.K. and Sweden on how algorithms on Facebook, Twitter, TikTok and YouTube contribute to spreading antisemitism.

What does antisemitism on social media look like?

Hatred against Jews on social media is often expressed in stereotypical depictions of Jews that stem from Nazi propaganda or in denial of the Holocaust.

Antisemitic social media posts also express hatred toward Jews that is based on the notion that all Jews are Zionist – that is, they are part of the national movement supporting Israel as a Jewish state – and Zionism is constructed as innately evil.

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However, today’s antisemitism is not only directed at Israelis, and it does not always take the form of traditional slogans or hate speech. Contemporary antisemitism manifests itself in various forms such as GIFs, memes, vlogs, comments and reactions such as likes and dislikes on the platforms.

Scholar Sophie Schmalenberger found that antisemitism is expressed not just in blunt, hurtful language and images on social media, but also in coded forms that may easily remain undetected. For example, on Facebook, Germany’s radical right-wing party Alternative für Deutschland, or AfD, omits the mentioning of the Holocaust in posts about the Second World War. It also uses antisemitic language and rhetoric that present antisemitism as acceptable.

Antisemitism may take on subtle forms such as in emojis. The emoji combination of a star of David, a Jewish symbol, and a rat resembles the Nazi propaganda likening Jews to vermin. In Nazi Germany, the constant repetition and normalization of such depictions led to the dehumanization of Jews and eventually the acceptance of genocide.

Other forms of antisemitism on social media are antisemitic troll attacks: Users organize to disrupt online events by flooding them with messages that deny the Holocaust or spread conspiracy myths as QAnon does.

Scholars Gabi Weimann and Natalie Masri have studied TikTok. They found that kids and young adults are especially in danger of being exposed, often unwittingly, to antisemitism on the very popular and fast-growing platform, which already counts over 1 billion users worldwide. Some of the content that is posted combines clips of footage from Nazi Germany with new text belittling or making fun of the victims of the Holocaust.

The continuous exposure to antisemitic content at a young age, scholars say, can lead to both normalization of the content and radicalization of the Tik-Tok viewer.

Algorithmic antisemitism

Antisemitism is fueled by algorithms, which are programmed to register engagement. This ensures that the more engagement a post receives, the more users see it. Engagement includes all reactions such as likes and dislikes, shares and comments, including countercomments. The problem is that reactions to posts also trigger rewarding dopamine hits in users. Because outrageous content creates the most engagement, users feel more encouraged to post hateful content.

However, even social media users who post critical comments on hateful content don’t realize that because of the way algorithms work, they end up contributing to its spread.

Research on video recommendations on YouTube also shows how algorithms gradually lead users to more radical content. Algorithmic antisemitism is thus a form of what criminologist Matthew Williams calls “algorithmic hate” in his book “The Science of Hate.”

What can be done about it?

To combat antisemitism on social media, strategies need to be evidence based. But neither social media companies nor researchers have devoted enough time and resources to this issue so far.

The study of antisemitism on social media poses unique challenges to researchers: They need access to the data and funding to be able to help develop effective counterstrategies. So far, scholars depend on the cooperation of the social media companies to access the data, which is mostly unregulated.

Social media companies have implemented guidelines on reporting antisemitism on social media, and civil society organizations have been demanding action against algorithmic antisemitism. However, the measures taken so far are woefully inadequate, if not dangerous. For example, counterspeech, which is often promoted as a possible strategy, tends to amplify hateful content.

To meaningfully address antisemitic hate speech, social media companies would need to change the algorithms that collect and curate user data for advertisement companies, which make up a large part of their revenue.

There is a global, borderless spread of antisemitic posts on social media happening on an unprecedented scale. We believe it will require the collective efforts of social media companies, researchers and civil society to combat this problem.

Sabine von Mering is director of Center for German and European Studies at Brandeis University. Monika Hübscher is a research associate and Ph.D. Candidate at the University of Duisburg-Essen. 

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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