A Survey on Automatic Detection of Hate Speech in Text

Fortuna, Paula; Nunes, Sérgio

doi:10.1145/3232676

Cited by 779 publications

(655 citation statements)

References 34 publications

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“…While the problem of online hate speech has been the focus of a wide body of research during the last few years [15], computational approaches targeting the problem of misogyny in particular are scarce and very recent. Computational methods have been either used to observe and study the phenomenon of online misogyny [6,[20][21][22], to generate automatic misogynistic content detection methods [4,12,13], or to use the appearance of misogyny related words in online content as a predictor of criminal behaviour [16].…”

Section: Computational Approachesmentioning

confidence: 99%

Exploring Misogyny across the Manosphere in Reddit

Farrell

Fernández

Novotný³

et al. 2019

Proceedings of the 10th ACM Conference on Web Science

188

113

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The 'manosphere' has been a recent subject of feminist scholarship on the web. Serious accusations have been levied against it for its role in encouraging misogyny and violent threats towards women online, as well as for potentially radicalising lonely or disenfranchised men. Feminist scholars evidence this through a shift in the language and interests of some men's rights activists on the manosphere, away from traditional subjects of family law or mental health and towards more sexually explicit, violent, racist and homophobic language. In this paper, we study this phenomenon by investigating the flow of extreme language across seven online communities on Reddit, with openly misogynistic members (e.g., Men Going Their Own Way, Involuntarily Celibates), and investigate if and how misogynistic ideas spread within and across these communities. Grounded on feminist critiques of language, we created nine lexicons capturing specific misogynistic rhetoric (Physical Violence, Sexual Violence, Hostility, Patriarchy, Stoicism, Racism, Homophobia, Belittling, and Flipped Narrative) and used these lexicons to explore how language evolves within and across misogynistic groups. This analysis was conducted on 6 million posts, from 300K conversations created between 2011 and December 2018. Our results shows increasing patterns on misogynistic content and users as well as violent attitudes, corroborating existing theories of feminist studies that the amount of misogyny, hostility and violence is steadily increasing in the manosphere.

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Section: Computational Approachesmentioning

confidence: 99%

Exploring Misogyny across the Manosphere in Reddit

Farrell

Fernández

Novotný³

et al. 2019

Proceedings of the 10th ACM Conference on Web Science

188

113

View full text Add to dashboard Cite

show abstract

“…Only legitimate people who are actively engaged in the same culture and who can be competent enough can give the answers to these questions. Some studies have given some necessary terminologies for studying hate speech, for example Fortuna and Nunes [12] have listed some of the main rules for hate speech identification. In brief, hate speech is identified when disparaging stereotype about group.…”

Section: What Constitutes Hate Speechmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

119

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In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

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“…To detect online hate speech, a large number of scientific studies have been dedicated by using Natural Language Processing (NLP) in combination with Machine Learning (ML) and Deep Learning (DL) methods [1,8,11,13,22,25]. Although supervised machine learning-based approaches have used different text mining-based features such as surface features, sentiment analysis, lexical resources, linguistic features, knowledge-based features or user-based and platformbased metadata [3,6,23], they necessitate a well-defined feature extraction approach. The trend now seems to be changing direction, with deep learning models being used for both feature extraction and the training of classifiers.…”

Section: Introductionmentioning

confidence: 99%

A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

Mozafari

Farahbakhsh

Crespi

2019

Studies in Computational Intelligence

263

173

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Generated hateful and toxic content by a portion of users in social media is a rising phenomenon that motivated researchers to dedicate substantial efforts to the challenging direction of hateful content identification. We not only need an efficient automatic hate speech detection model based on advanced machine learning and natural language processing, but also a sufficiently large amount of annotated data to train a model. The lack of a sufficient amount of labelled hate speech data, along with the existing biases, has been the main issue in this domain of research. To address these needs, in this study we introduce a novel transfer learning approach based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers). More specifically, we investigate the ability of BERT at capturing hateful context within social media content by using new finetuning methods based on transfer learning. To evaluate our proposed approach, we use two publicly available datasets that have been annotated for racism, sexism, hate, or offensive content on Twitter. The results show that our solution obtains considerable performance on these datasets in terms of precision and recall in comparison to existing approaches. Consequently, our model can capture some biases in data annotation and collection process and can potentially lead us to a more accurate model.

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A Survey on Automatic Detection of Hate Speech in Text

Cited by 779 publications

References 34 publications

Exploring Misogyny across the Manosphere in Reddit

Exploring Misogyny across the Manosphere in Reddit

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

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