The Susceptibility Paradox in Online Social Influence

2024-06-17

arXiv.org

AI Summary

This paper investigates the mechanisms underlying users’ susceptibility to influence in online social networks, aiming to distinguish influence-driven adoption from spontaneous adoption to enable precise content governance. Method: Leveraging large-scale behavioral logs, we employ causal inference, network homophily analysis, and susceptibility modeling. Contribution/Results: We first identify and empirically validate the “Susceptibility Paradox”: a user’s friends exhibit significantly higher average susceptibility than the user themselves—a phenomenon observed exclusively in influence-driven adoption (characterized by strong homophily) and absent in spontaneous adoption. Crucially, we demonstrate that an individual’s susceptibility to influence can be predicted with high accuracy using only their friends’ susceptibility scores, without requiring any user metadata. By extending the friendship paradox to the behavioral dimension of influence, this work provides an interpretable, deployable theoretical foundation and methodological framework for identifying vulnerable users and mitigating misinformation diffusion.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

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.

Problem

• Investigates susceptibility to influence in social networks
• Explores differential effects of influence-driven versus spontaneous behaviors
• Examines predictability of influence susceptibility using friends' traits

Innovation

• Analyzes influence-driven versus spontaneous content adoption
• Extends Generalized Friendship Paradox to influence susceptibility
• Predicts influence susceptibility using friends' susceptibility data