Detecting conspiracy theories on social media : improving machine learning to detect and understand online conspiracy theories / William Marcellino, Todd C. Helmus, Joshua Kerrigan, Hilary Reininger, Rouslan I. Karimov, Rebecca Ann Lawrence.
Includes bibliographical references (pages 73-85).
Contents:
Introduction: Detecting and Understanding Online Conspiracy Language -- Making Sense of Conspiracy Theories -- Modeling Conspiracy Theories: A Hybrid Approach -- Conclusion and Recommendations -- Appendix A: Data and Methodology -- Appendix B: Stance: Text Analysis and Machine Learning.
Summary:
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.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.