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Found 8 papers, 5 papers with code
Recalibrating classifiers for interpretable abusive content detection
We investigate the use of machine learning classifiers for detecting online abuse in empirical research.
An influencer-based approach to understanding radical right viral tweets
We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets.
Introducing CAD: the Contextual Abuse Dataset
Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic.
An Expert Annotated Dataset for the Detection of Online Misogyny
Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women.
HateCheck: Functional Tests for Hate Speech Detection Models
Detecting online hate is a difficult task that even state-of-the-art models struggle with.
Detecting East Asian Prejudice on Social Media
The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic.
Islamophobes are not all the same! A study of far right actors on Twitter
Far-right actors are often purveyors of Islamophobic hate speech online, using social media to spread divisive and prejudiced messages which can stir up intergroup tensions and conflict.
Social and Information Networks Computers and Society Physics and Society Applications
Challenges and frontiers in abusive content detection
Online abusive content detection is an inherently difficult task.
