EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Deplatforming and Online Hate Speech Across the Social Media Ecology

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Social media platforms have recently begun to respond to pressure to remove or "deplatform" harmful content and malicious actors. This project analyzes whether current deplatforming strategies mitigate harmful communication. The project analyzes whether removing hate speech and extremist actors and communities curbs the spread of malicious content throughout social media. The analysis also can reveal whether whether deplatformed actors regroup on fringe platforms, reappear in potentially larger numbers, grow more cohesive communities, or attract a more extreme following with increased levels of hateful content, perhaps on platforms that do not moderate this content. Through this research, the project provides a new understanding of how content moderation and deplatforming affect the development of online hate and extremism, leading to the design of effective policies to limit malicious content online.This project is collecting data on online hate clusters, or groups, across multiple social media platforms. The project team is constructing a machine-learning tool that can use these data to classify a wide range of hate speech types and targets, and the extent to which social media posts contain these types and targets. The team uses statistical models to analyze the effects of deplatforming events on the use, volume, and toxicity of hate speech, as well as on the extent to which groups reorganize on unmoderated platforms. The ultimate goal of the project is to build a dynamic, mathematical model to quantify how deplatforming affects the emergence of extremist groups and their evolution over time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.