Assessing the impact of contextual information in hate speech detection

Juan Manuel Pérez will give a talk during his visiting week to the Content-centered Computing group.

In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the incommensurable amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms.

One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not.

In this talk, I will comment on some experiments we have performed to assess the impact of context in hate speech detection. With this in mind, we built a contextualized dataset for hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic.

For the two proposed tasks using this novel corpus (binary detection; and granular detection, where the system has to predict the attacked characteristics), the classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance.

When: September 29, 2022, at 11:00

Where: Conference room 3rd floor (Sala Seminari)

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