BIBFRAME Work

Title

Detecting conspiracy theories on social media

Type

• Text
• Monograph

Contribution

• International Security and Defense Policy Center (contributor)
• Rand Corporation (contributor)
• Google (Firm) (contributor)
• Helmus, Todd C. (author )
• Marcellino, William (author )
• Kerrigan, Joshua (contributor)

Subject

• Conspiracy theories--United States--Psychological aspects (LCSH)
• Machine learning--Technological innovations (LCSH)
• Social media--Political aspects--United States (LCSH)
• Conspiracy theories--Political aspects--United States (LCSH)
• Communication in politics--Technological innovations--United States (LCSH)

Language

• English

Illustrative Content

• illustrations

Classification

LCC: JA85.2.U6 (Assigner: dlc) (Status: used by assigner)

Content

• text

Supplementary content

• bibliography

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.

Table of 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.

Authorized Access Point

Marcellino, William. Detecting conspiracy theories on social media

Geographic Coverage

• United States

Admin Metadata

• Date: 2026-09-11T20:12:39.653570+00:00
• Agent: cbc
• Status: c
• Derived from: 24033298

Admin Metadata

• Date: 2026-09-11
• Cataloger id: Unknown
• Description level: http://id.loc.gov/ontologies/bibframe-2-6-0/
• Description language: eng
• Description authentication: pcc