Hate Speech

Departing from Jessie Daniels’s 2013 review of scholarship on race and racism online, this article maps and discusses recent developments in the study of racism and hate speech in the subfield of social media research. Systematically examining 104 articles, we address three research questions: Which geographical contexts, platforms, and methods do researchers engage with in ...

Rassistische Hassrede im Internet: Wie soll die Schweiz in Zukunft damit umgehen? Der vorliegende Bericht, welchen die Fachstelle für Rassismusbekämpfung (FRB) in Auftrag gegeben hat, liefert dazu einen Beitrag. Er beinhaltet eine kritische Zusammenfassung der Datenlage, einen Einblick in die digitale Umwelt und egünstigenden Kommunikationsbedingungen rassistischer Online-Hassrede sowie einen Überblick zu bestehenden Gegenmassnahmen in der ...

We have collected over 30M messages from the publicly available /pol/ message boards on 4chan and 8chan, and compiled them into a model of toxic language use. The trained word embeddings (±0.4GB) are released for free and may be useful for further study on toxic discourse or to boost hate speech detection systems: textgain.com/8chan. ...

Hate speech and toxic communication online is on the rise. Responses to this issue tend to offer technical (automated) or non-technical (human content moderation) solutions, or see hate speech as a natural product of hateful people. In contrast, this article begins by recognizing platforms as designed environments that support particular practices while discouraging others. In ...

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then ...

The increasing presence of hate speech on social media has drawn significant investment from governments, companies, and empirical research. Existing methods typically use a supervised text classification approach that depends on carefully engineered features. However, it is unclear if these features contribute equally to the performance of such methods. We conduct a feature selection analysis ...

Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To ...

The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in ...

A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. ...

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple ...