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.2022 Sep 9:1-35.
doi: 10.1007/s10588-022-09363-2. Online ahead of print.

Differences between antisemitic and non-antisemitic English language tweets

Affiliations

Differences between antisemitic and non-antisemitic English language tweets

Gunther Jikeli et al. Comput Math Organ Theory..

Abstract

Antisemitism is a global phenomenon on the rise that is negatively affecting Jews and communities more broadly. It has been argued that social media has opened up new opportunities for antisemites to disseminate material and organize. It is, therefore, necessary to get a picture of the scope and nature of antisemitism on social media. However, identifying antisemitic messages in large datasets is not trivial and more work is needed in this area. In this paper, we present and describe an annotated dataset that can be used to train tweet classifiers. We first explain how we created our dataset and approached identifying antisemitic content by experts. We then describe the annotated data, where 11% of conversations about Jews (January 2019-August 2020) and 13% of conversations about Israel (January-August 2020) were labeled antisemitic. Another important finding concerns lexical differences across queries and labels. We find that antisemitic content often relates to conspiracies of Jewish global dominance, the Middle East conflict, and the Holocaust.

Keywords: Antisemitism; Hate speech; Twitter; White supremacy.

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

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Figures

Fig. 1
Fig. 1
Word clouds showing similarities and differences between tweet types
Fig. 2
Fig. 2
Word graph using top 50 words for each of the eight annotation types, resulting in 238 unique words
Fig. 3
Fig. 3
Antisemitic tweets include each sentiment type
Fig. 4
Fig. 4
Non-antisemitic tweets also include each sentiment type
Fig. 5
Fig. 5
Distributions for bot scores for three user types: those that have only tweeted antisemitic tweets, those that never tweeted antisemitic tweets, and those that have tweeted both types
Fig. 6
Fig. 6
Kernel density estimate of the joint distribution for bot scores and the number of retweets already accumulated by a tweet when retweeted by the user in question. Kernel density estimates of the marginal distributions are also provided (top and right)
Fig. 7
Fig. 7
Example URL sites used in annotated tweets
Fig. 8
Fig. 8
Timeline of tweets for the 'Jews' query
Fig. 9
Fig. 9
Timeline of tweets for the 'Israel' query
See this image and copyright information in PMC

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References

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