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The temporal evolution of a far-right forum

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  • Bennett Kleinberg

    (University College London
    University College London)

  • Isabelle Vegt

    (University College London)

  • Paul Gill

    (University College London)

Abstract

The increased threat of right-wing extremist violence necessitates a better understanding of online extremism. Radical message boards, small-scale social media platforms, and other internet fringes have been reported to fuel hatred. The current paper examines data from the right-wing forum Stormfront between 2001 and 2015. We specifically aim to understand the development of user activity and the use of extremist language. Various time-series models depict posting frequency and the prevalence and intensity of extremist language. Individual user analyses examine whether some super users dominate the forum. The results suggest that structural break models capture the forum evolution better than stationary or linear change models. We observed an increase of forum engagement followed by a decrease towards the end of the time range. However, the proportion of extremist language on the forum increased in a step-wise matter until the early summer of 2011, followed by a decrease. This temporal development suggests that forum rhetoric did not necessarily become more extreme over time. Individual user analysis revealed that super forum users accounted for the vast majority of posts and of extremist language. These users differed from normal users in their evolution of forum engagement.

Suggested Citation

  • Bennett Kleinberg & Isabelle Vegt & Paul Gill, 2021. "The temporal evolution of a far-right forum," Journal of Computational Social Science, Springer, vol. 4(1), pages 1-23, May.
    • Handle: RePEc:spr:jcsosc:v:4:y:2021:i:1:d:10.1007_s42001-020-00064-x
    DOI: 10.1007/s42001-020-00064-x
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    References listed on IDEAS

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    Cited by:

    1. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.

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