How fake news can turn against its spreader

2025-04-23

AI Summary

This paper investigates a counterintuitive effect of misinformation in multi-source co-occurrence scenarios: when a truthful source exhibits sufficiently high signal-to-noise ratio and noise across true and false sources is positively correlated, exposure to misinformation paradoxically leads rational Bayesian receivers to form beliefs opposite to the disinformation’s intended direction. Method: We model multi-source information fusion within a rigorous Bayesian updating framework, explicitly incorporating correlated noise structures across heterogeneous sources. Contribution/Results: We provide the first formal proof of the existence conditions for this “self-reversal effect” and derive closed-form analytical thresholds governing its onset. This finding challenges conventional active debunking paradigms—reliant on content identification and labeling—and instead establishes a theoretically grounded, quantitatively verifiable foundation for passive, audit-free resilience against misinformation: such resilience emerges endogenously from structural properties of information environments, without requiring explicit fact-checking or content moderation.

Technology Category

Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)Application Domains: Misinformation & Fake NewsReasoning under Uncertainty: Causality

Application Category

Responsible Web: Mitigating misinformation and disinformationWeb Mining and Content Analysis: Content-based information diffusionUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

When different information sources on a given topic are combined, they interact in a nontrivial manner for a rational receiver of these information sources. Suppose that there are two information sources, one is genuine and the other contains disinformation. It is shown that under the conditions that the signal-to-noise ratio of the genuine information source is sufficiently large, and that the noise terms in the two information sources are positively correlated, the effect of disinformation is reversed from its original intent. That is, the effect of disinformation on a receiver of both information sources, who is unaware of the existence of disinformation, is to generate an opposite interpretation. While the condition in which this phenomenon occurs cannot always be ensured, when it is satisfied, the effect provides an effective way of countering the impacts of disinformation.

Problem

• How fake news reverses its intended effect on receivers
• Conditions for disinformation to backfire on its spreader
• Countering disinformation via correlated genuine and fake sources

Innovation

• Combines genuine and disinformation sources interactively
• Uses signal-to-noise ratio for genuine source
• Leverages positive noise correlation for reversal