Conspiracy theories circulated online contribute to a shift in public discourse away from facts and analysis and can contribute to public harm. Using linguistic and rhetorical theory, RAND researchers conducted a modeling effort to improve machine-learning technology for detecting conspiracy theory language. This report describes the results of that effort and offers recommendations to counter the effects of online conspiracy theories.

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

ResearchPublished Apr 29, 2021

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.

Key Findings

  • 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.

Recommendations

  • Engage transparently and empathetically with conspiracists.
  • Correct conspiracy-related false news.
  • Engage with moderate members of conspiracy groups.
  • Address fears and existential threats.

Order a Print Copy

Format
Paperback
Page count
108 pages
List Price
$22.50
Buy link
Add to Cart

Topics

Document Details

  • Copyright: RAND Corporation
  • Availability: Available
  • Year: 2021
  • Print Format: Paperback
  • Paperback Pages: 108
  • Paperback Price: $22.50
  • Paperback ISBN/EAN: 978-1-9774-0689-7
  • DOI: https://doi.org/10.7249/RR-A676-1
  • Document Number: RR-A676-1

Citation

RAND Style Manual
Marcellino, William, Todd C. Helmus, Joshua Kerrigan, Hilary Reininger, Rouslan I. Karimov, and Rebecca Ann Lawrence, Detecting Conspiracy Theories on Social Media: Improving Machine Learning to Detect and Understand Online Conspiracy Theories, RAND Corporation, RR-A676-1, 2021. As of October 3, 2024: https://www.rand.org/pubs/research_reports/RRA676-1.html
Chicago Manual of Style
Marcellino, William, Todd C. Helmus, Joshua Kerrigan, Hilary Reininger, Rouslan I. Karimov, and Rebecca Ann Lawrence, Detecting Conspiracy Theories on Social Media: Improving Machine Learning to Detect and Understand Online Conspiracy Theories. Santa Monica, CA: RAND Corporation, 2021. https://www.rand.org/pubs/research_reports/RRA676-1.html. Also available in print form.
BibTeX RIS

Research conducted by

This research was sponsored by Google's Jigsaw unit and conducted within the International Security and Defense Policy Center of the RAND National Security Research Division (NSRD).

This publication is part of the RAND research report series. Research reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND research reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.