Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere

Albadi, Nuha; Kurdi, Maram; Mishra, Shivakant

doi:10.1109/asonam.2018.8508247

Cited by 151 publications

(152 citation statements)

References 19 publications

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“…Hee et al (2015) detect bullies and victims among youngsters in Dutch comments on AskFM, and classify cyberbullying comments as insults or threats. Moreover, Albadi et al (2018) provide a corpus of Arabic sectarian speech.…”

Section: Related Workmentioning

confidence: 99%

Multilingual and Multi-Aspect Hate Speech Analysis

Ousidhoum

Lin

Zhang

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

189

159

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Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art multilingual multitask learning approaches. We evaluate our dataset in various classification settings, then we discuss how to leverage our annotations in order to improve hate speech detection and classification in general. 4 https://competitions.codalab.org/competitions/19935

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Section: Related Workmentioning

confidence: 99%

Multilingual and Multi-Aspect Hate Speech Analysis

Ousidhoum

Lin

Zhang

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

189

159

View full text Add to dashboard Cite

show abstract

“…Our previous work [3,4] appears to be the only one focusing on hate speech detection and analysis in Arabic social media. Our study revealed that religious hate speech is widespread on Arabic Twitter.…”

Section: Online Hate Speechmentioning

confidence: 99%

“…In our recent work on hate speech in Arabic social media [3,4], we showed that Arabic Twitter is awash with religious hatred which we defined as "a speech that is insulting, offensive, or hurtful and is intended to incite hate, discrimination, or violence against an individual or a group of people on the basis of religious beliefs or lack thereof". Having such a large volume of hate speech and knowing that ISIS and other radical organizations have been using bots to push their extreme ideologies [6,7], we hypothesize that bots may be to blame for a significant amount of this widespread hatred.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we build a novel regression model, based on linguistic, content, behavioral and topic features to detect Arabic Twitter bots to understand the impact of bots in spreading religious hatred in Arabic Twitter space. In particular, we quantitatively code and analyze a representative sample of 450 accounts disseminating hate speech from the dataset constructed in our previous work [3,4] for bot-like behavior. We compare our assigned bot-likelihood scores to those of Botometer [18], a well-known machine-learning-based bot detection tool, and we show that Botometer performs a little above average in detecting Arabic bots.…”

Section: Introductionmentioning

confidence: 99%

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Hateful People or Hateful Bots?

Albadi

Kurdi

Mishra

2019

Proc. ACM Hum.-Comput. Interact.

Self Cite

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Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.

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“…A particular facet of religious discussion on Arab social media is hate speech about both religion and atheism [1]. The study by Albadi et al shows that 42% of the studied tweets (n=6000) that cross-reference religions contain hate-speech.…”

Section: Social Media Globally and In The Arab Societiesmentioning

confidence: 99%

Arabs and Atheism: Religious Discussions in the Arab Twittersphere

Hariri

Magdy

Wolters

2019

Lecture Notes in Computer Science

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Most previous research on online discussions of atheism has focused on atheism within a Christian context. In contrast, discussions about atheism in the Arab world and from Islamic background are relatively poorly studied. An added complication is that open atheism is against the law in some Arab countries, which may further restrict atheist activity on social media. In this work, we explore atheistic discussion in the Arab Twittersphere. We identify four relevant categories of Twitter users according to the content they post: atheistic, theistic, tanweeri (religious renewal), and other. We characterise the typical content posted by these four sets of users and their social networks, paying particular attention to the topics discussed and the interaction among them. Our findings have implication for the study of religious and spiritual discourse on social media and provide a better cross-cultural understanding of relevant aspects.

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Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere

Cited by 151 publications

References 19 publications

Multilingual and Multi-Aspect Hate Speech Analysis

Multilingual and Multi-Aspect Hate Speech Analysis

Hateful People or Hateful Bots?

Arabs and Atheism: Religious Discussions in the Arab Twittersphere

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