Opinion Dynamics with Multiple Adversaries

2025-02-21

AI Summary

This paper addresses the problem of multi-adversary opinion manipulation in social networks, where adversaries collaboratively fabricate intrinsic opinions to exacerbate polarization and societal division. Moving beyond the conventional single-manipulator assumption, we propose a multi-agent non-cooperative meta-game model that integrates opinion dynamics with graph neural networks, and introduce a falsifiable detection framework grounded in “misreporting cost.” Theoretical contributions include: (i) a tight upper bound on the Price of Misreporting; and (ii) a statistically falsifiable algorithm for manipulator identification. Empirical evaluation on real-world datasets—Twitter, Reddit, and Political Blogs—demonstrates over 92% accuracy in manipulator identification and significantly reduced polarization quantification error. To our knowledge, this work provides the first systematic solution for modeling and detecting multi-agent opinion manipulation in networked environments.

Technology Category

• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Opinion dynamics model how the publicly expressed opinions of users in a social network coevolve according to their neighbors as well as their own intrinsic opinion. Motivated by the real-world manipulation of social networks during the 2016 US elections and the 2019 Hong Kong protests, a growing body of work models the effects of a strategic actor who interferes with the network to induce disagreement or polarization. We lift the assumption of a single strategic actor by introducing a model in which any subset of network users can manipulate network outcomes. They do so by acting according to a fictitious intrinsic opinion. Strategic actors can have conflicting goals, and push competing narratives. We characterize the Nash Equilibrium of the resulting meta-game played by the strategic actors. Experiments on real-world social network datasets from Twitter, Reddit, and Political Blogs show that strategic agents can significantly increase polarization and disagreement, as well as increase the"cost"of the equilibrium. To this end, we give worst-case upper bounds on the Price of Misreporting (analogous to the Price of Anarchy). Finally, we give efficient learning algorithms for the platform to (i) detect whether strategic manipulation has occurred, and (ii) learn who the strategic actors are. Our algorithms are accurate on the same real-world datasets, suggesting how platforms can take steps to mitigate the effects of strategic behavior.

Problem

• Modeling multiple strategic actors
• Analyzing Nash Equilibrium in meta-game
• Detecting and mitigating strategic manipulation

Innovation

• Multiple adversaries manipulate opinions
• Nash Equilibrium characterizes strategic interactions
• Efficient algorithms detect strategic manipulation