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• The hybrid ML model improved conspiracy topic detection.
• The hybrid ML model dramatically improved on either single model's ability to detect conspiratorial language.
• Hybrid models likely have broad application to detecting any kind of harmful speech, not just that related to conspiracy theories.
• Some conspiracy theories, though harmful, rhetorically invoke legitimate social goods, such as health and safety.
• Some conspiracy theories rhetorically function by creating hate-based "us versus them" social oppositions.
• Direct contradiction or mockery is unlikely to change conspiracy theory adherence.

Conspiracy theories circulated online via social media contribute to a shift in public discourse away from facts and analysis and can contribute to direct public harm. Social media platforms face a difficult technical and policy challenge in trying to mitigate harm from online conspiracy theory language. As part of Google's Jigsaw unit's effort to confront emerging threats and incubate new technology to help create a safer world, RAND researchers conducted a modeling effort to improve machine-learning (ML) technology for detecting conspiracy theory language. They developed a hybrid model using linguistic and rhetorical theory to boost performance. They also aimed to synthesize existing research on conspiracy theories using new insight from this improved modeling effort. This report describes the results of that effort and offers recommendations to counter the effects of conspiracy theories that are spread online.

• Chapter One

  Introduction: Detecting and Understanding Online Conspiracy Language

• Chapter Two

  Making Sense of Conspiracy Theories

• Chapter Three

  Modeling Conspiracy Theories: A Hybrid Approach

• Chapter Four

  Conclusion and Recommendations

• Appendix A

  Data and Methodology

• Appendix B

  Stance: Text Analysis and Machine Learning