Abstract
Online hate speech can precipitate and also follow real-world violence, such as the U.S. Capitol attack on January 6, 2021. However, the current volume of content and the wide variety of extremist narratives raise major challenges for social media companies in terms of tracking and mitigating the activity of hate groups and broader extremist movements. This is further complicated by the fact that hate groups and extremists can leverage multiple platforms in tandem in order to adapt and circumvent content moderation within any given platform (e.g. Facebook). We show how the computational approach of dynamic Latent Dirichlet Allocation (LDA) may be applied to analyze similarities and differences between online content that is shared across social media platforms by extremist communities, including Facebook, Gab, Telegram, and VK between January and April 2021. We also discuss characteristics revealed by unsupervised machine learning about how hate groups leverage sites to organize, recruit, and coordinate within and across such online platforms.
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Acknowledgments
We acknowledge Rhys Leahy and Nicolás Velásquez for their help finding and downloading the data used in this study.
We are grateful for funding for this research from the U.S. Air Force Office of Scientific Research under award numbers FA9550-20-1-0382 and FA9550-20-1-0383. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force.
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Sear, R., Restrepo, N.J., Lupu, Y., Johnson, N.F. (2022). Dynamic Topic Modeling Reveals Variations in Online Hate Narratives. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_38
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