The Susceptibility Paradox in Online Social Influence
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Abstract (arXiv)
Understanding susceptibility to online influence is crucial for mitigating the spread of misinformation and protecting vulnerable audiences. This paper investigates susceptibility to influence within social networks, focusing on the differential effects of influence-driven versus spontaneous behaviors on user content adoption. Our analysis reveals that influence-driven adoption exhibits high homophily, indicating that individuals prone to influence often connect with similarly susceptible peers, thereby reinforcing peer influence dynamics, whereas spontaneous adoption shows significant but lower homophily. Additionally, we extend the Generalized Friendship Paradox to influence-driven behaviors, demonstrating that users' friends are generally more susceptible to influence than the users themselves, de facto establishing the notion of Susceptibility Paradox in online social influence. This pattern does not hold for spontaneous behaviors, where friends exhibit fewer spontaneous adoptions. We find that susceptibility to influence can be predicted using friends' susceptibility alone, while predicting spontaneous adoption requires additional features, such as user metadata. These findings highlight the complex interplay between user engagement and characteristics in spontaneous content adoption. Our results provide new insights into social influence mechanisms and offer implications for designing more effective moderation strategies to protect vulnerable audiences.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2024.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operationscited by 6
- 2026Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibilitycited by 5
- 2025Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalancecited by 4
- 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
- 2026Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility4 Feb 2026
- 2025Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations28 Oct 2025
- 2025Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance21 Jan 2025