Challenges of Deplatforming Extremist Online Movements: A Machine-Learning Approach

Online extremist movements are increasingly using social media communities to share content, spread their ideologies, recruit members, and mobilize offline activities. In recent years, mainstream platforms, including Twitter and Facebook, have adopted policies to remove or deplatform some of these movements. Yet online extremists are well-known for their abilities to adapt, self-censor, and migrate across online platforms. How successful have these extremist movement deplatformings been? To answer this question, we begin by training a classifier to identify content generated by four prominent extremist movements: white supremacists, patriot/militia groups, QAnon, and Boogaloos. After doing so, we use this classifier to analyze approximately 12 million posts generated by about 1500 online hate communities across 8 social media platforms, including both mainstream and alternative platforms. We find that the deplatformings of Boogaloos and QAnon by mainstream platforms were initially highly successful, but that both movements were able to find ways to re-introduce their content on these platforms. These findings highlight the challenges of movement-based deplatforming, and they point toward important implications for content moderation.

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Tags: Boogaloos, Deplatforming, machine learning, QAnon, White Supremacy