Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Nov 2023.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 2.
6 papers.
- 2023Leveraging Large Language Models to Detect Influence Campaigns in Social Mediacited by 13
- 2023Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Datacited by 11
- 2025GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classificationcited by 7
- 2025Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operationscited by 6
- 2026Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.aicited by 1
- 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behaviorcited by 0
- 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior17 Aug 2026
- 2026Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai3 Jan 2026
- 2025Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations28 Oct 2025
- 2025GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classification6 Jan 2025
- 2023Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data31 Dec 2023
- 2023Leveraging Large Language Models to Detect Influence Campaigns in Social Media14 Nov 2023