Social Media

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 ...

Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution ...

The paper describes a recently-created Twitter corpus of about 6,000 tweets, annotated for hate speech against immigrants, and developed to be a reference dataset for an automatic system of hate speech monitoring. The annotation scheme was therefore specifically designed to account for the multiplicity of factors that can contribute to the definition of a hate ...

Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature ...

Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to ...

The briefing highlights how through its limited content moderation policies Telegram has become a safe space for white supremacists to share and discuss a range of explicit extremist material. Furthermore, it shows that through the many of these Telegram communities have become permissive environments where overt calls for violence and support for terrorism is widespread. ...

This study looks at how terror attacks are rendered discursively meaningful on social media through the concurrent use and reiteration of terror hashtags, which were created following previous incidents of terror. The article focuses on 12 terror attacks in Europe in 2015-2017 and their relating hashtags on Twitter, to see how various combinations of these ...

This report represents the interim findings of a two-year study designed to increase understanding of the social media footprint of right-wing extremism (RWE) in Canada. This work is part of a larger project designed to understand RWE in Canada led by Ontario Tech University (OTU), in partnership with Michigan State University and the University of ...