Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure

TL;DR

This paper addresses how exposure to information sources of varying credibility influences user adoption on Twitter, focusing on political and public health discourse. It introduces an exposure–adoption framework that infers exposure from user interactions and defines adoption as URL sharing, while credibility is assigned via MBFC and the Iffy Index and quantified through exposure and adoption metrics. The study finds that adoption probability grows with exposure frequency, low-credibility sources are adopted with fewer exposures than high-credibility ones, and extremal credibility levels prompt faster adoption; moreover, exposure credibility strongly predicts adoption credibility, revealing credibility-aligned echo-chamber dynamics. These findings have practical implications for platform design and policy, suggesting targeted interventions to reduce misinformation spread and promote high-credibility content, while acknowledging methodological limitations and ethical considerations.

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.

Figures (12)

• Figure 1: RQs and components of the proposed methodological framework.
• Figure 2: The probability of adoption at varying levels of exposures with a logarithmic regression fit. Each data point represents a specific instance of adoption probability $Pr(A|n_{e})$ at a given exposure $n_{e}$.
• Figure 3: The probability of adoption at varying levels of exposures with a logarithmic regression fit, modulated by the credibility of information sources.
• Figure 4: The probability of adoption given a number of exposures $n_{e}\le250$, modulated by user groups.
• Figure 5: Exposure level of the top 15 most frequently adopted information sources at varying credibility for the Covid dataset. Each data point in the simplex is an information source, and the position of the point represents the percentage (%) of instances where the information source was adopted at low/medium/high exposure levels.