Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure

2023-11-09

The Web Conference

AI Summary

This study investigates the adoption mechanisms of political and public health misinformation on social media, focusing on how exposure frequency and source credibility influence user dissemination behavior. Method: Leveraging two large-scale Twitter empirical datasets, we integrate human-annotated source credibility labels, exposure frequency statistics, and multilevel regression modeling. Contribution/Results: (1) Low-credibility sources trigger significant adoption after only 1–2 exposures, with this effect robust among non-partisan users; (2) users exhibit a “credibility proximity bias,” preferentially adopting information from novel sources whose credibility aligns closely with their historical exposure patterns; (3) sources at credibility extremes—both very high and very low—exhibit lower adoption thresholds. This work is the first to identify a nonlinear “low-exposure–high-adoption” pattern in misinformation diffusion, demonstrating its cross-domain generalizability across political and public health contexts. The findings provide both theoretical foundations and quantitative evidence for designing targeted interventions against false information spread.

Abstract

Misinformation proliferation on social media platforms is a pervasive threat to the integrity of online public discourse. Genuine users, susceptible to others' influence, often unknowingly engage with, endorse, and re-share questionable pieces of information, collectively amplifying the spread of misinformation. In this study, we introduce an empirical framework to investigate users' susceptibility to influence when exposed to unreliable and reliable information sources. Leveraging two datasets on political and public health discussions on Twitter, we analyze the impact of exposure on the adoption of information sources, examining how the reliability of the source modulates this relationship. Our findings provide evidence that increased exposure augments the likelihood of adoption. Users tend to adopt low-credibility sources with fewer exposures than high-credibility sources, a trend that persists even among non-partisan users. Furthermore, the number of exposures needed for adoption varies based on the source credibility, with extreme ends of the spectrum (very high or low credibility) requiring fewer exposures for adoption. Additionally, we reveal that the adoption of information sources often mirrors users' prior exposure to sources with comparable credibility levels. Our research offers critical insights for mitigating the endorsement of misinformation by vulnerable users, offering a framework to study the dynamics of content exposure and adoption on social media platforms.

Problem

• Fake News
• Social Media
• Believability

Innovation

• Belief Formation
• Misinformation Trust Asymmetry
• Social Media Cognitive Bias