May 11, 2021

Conference Paper: “‘Like Sheep Among Wolves’: Characterizing Hateful Users on Twitter”

The following paper will be presented at the MIS2 Workshop @ WSDM’18 (The 11th ACM International Conference on Web Search and Data Mining) scheduled to take place next month in Los Angeles. We are sharing a copy of the paper that was recently made available by the authors via arXiv.

Title

Like Sheep Among Wolves’: Characterizing Hateful Users on Twitter

Authors

Manoel Horta Ribeiro
Universidade Federal de Minas Gerais, Brazil

Pedro H. Calais
Universidade Federal de Minas Gerais, Brazil

Yuri A. Santos
Universidade Federal de Minas Gerais, Brazil

Virgílio A. F. Almeida
Universidade Federal de Minas Gerais, Brazil

Wagner Meira Jr
Universidade Federal de Minas Gerais, Brazil

Source

via arXiv

Abstract

Hateful speech in Online Social Networks (OSNs) is a key challenge for companies and governments, as it impacts users and advertisers, and as several countries have strict legislation against the practice. This has motivated work on detecting and characterizing the phenomenon in tweets, social media posts and comments. However, these approaches face several shortcomings due to the noisiness of OSN data, the sparsity of the phenomenon, and the subjectivity of the definition of hate speech. This works presents a user-centric view of hate speech, paving the way for better detection methods and understanding. We collect a Twitter dataset of 100,386 users along with up to 200 tweets from their timelines with a random-walk-based crawler on the retweet graph, and select a subsample of 4,972 to be manually annotated as hateful or not through crowdsourcing. We examine the difference between user activity patterns, the content disseminated between hateful and normal users, and network centrality measurements in the sampled graph. Our results show that hateful users have more recent account creation dates, and more statuses, and followees per day. Additionally, they favorite more tweets, tweet in shorter intervals and are more central in the retweet network, contradicting the “lone wolf” stereotype often associated with such behavior. Hateful users are more negative, more profane, and use less words associated with topics such as hate, terrorism, violence and anger. We also identify similarities between hateful/normal users and their 1-neighborhood, suggesting strong homophily.

Direct to Full Text Paper
8 pages; PDF.

About Gary Price

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

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