Paul Gill

Isabelle van der Vegt, Maximilian Mozes, Bennett Kleinberg et al.

This paper introduces the Grievance Dictionary, a psycholinguistic dictionary which can be used to automatically understand language use in the context of grievance-fuelled violence threat assessment. We describe the development the dictionary, which was informed by suggestions from experienced threat assessment practitioners. These suggestions and subsequent human and computational word list generation resulted in a dictionary of 20,502 words annotated by 2,318 participants. The dictionary was validated by applying it to texts written by violent and non-violent individuals, showing strong evidence for a difference between populations in several dictionary categories. Further classification tasks showed promising performance, but future improvements are still needed. Finally, we provide instructions and suggestions for the use of the Grievance Dictionary by security professionals and (violence) researchers.

Isabelle van der Vegt, Bennett Kleinberg, Paul Gill

The problem of online threats and abuse could potentially be mitigated with a computational approach, where sources of abuse are better understood or identified through author profiling. However, abusive language constitutes a specific domain of language for which it has not yet been tested whether differences emerge based on a text author's personality, age, or gender. This study examines statistical relationships between author demographics and abusive vs normal language, and performs prediction experiments for personality, age, and gender. Although some statistical relationships were established between author characteristics and language use, these patterns did not translate to high prediction performance. Personality traits were predicted within 15% of their actual value, age was predicted with an error margin of 10 years, and gender was classified correctly in 70% of the cases. These results are poor when compared to previous research on author profiling, therefore we urge caution in applying this within the context of abusive language and threat assessment.

Isabelle van der Vegt, Maximilian Mozes, Paul Gill et al.

The media frequently describes the 2017 Charlottesville 'Unite the Right' rally as a turning point for the alt-right and white supremacist movements. Social movement theory suggests that the media attention and public discourse concerning the rally may have influenced the alt-right, but this has yet to be empirically tested. The current study investigates whether there are differences in language use between 7,142 alt-right and progressive YouTube channels, in addition to measuring possible changes as a result of the rally. To do so, we create structural topic models and measure bigram proportions in video transcripts, spanning eight weeks before to eight weeks after the rally. We observe differences in topics between the two groups, with the 'alternative influencers' for example discussing topics related to race and free speech to an increasing and larger extent than progressive channels. We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally. While most changes relate to mentions of the rally itself, the alternative group also shows an increase in promotion of their YouTube channels. Results are discussed in light of social movement theory, followed by a discussion of potential implications for understanding the alt-right and their language use on YouTube.