Technology Category
Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)Application Domains: Misinformation & Fake NewsMultiagent Systems: Coordination and Collaboration
Coordinated inauthentic behavior and information spreading on Twitter
🤖 AI Summary
This work investigates how coordinated inauthentic accounts (e.g., bot clusters) on Twitter manipulate information diffusion algorithms and amplify misinformation through temporally consistent anomalous behaviors. To address the limitation of existing methods—namely, their failure to model multi-level propagation dynamics—we propose the first systematic framework for modeling coordinated misinformation behavior. Our approach integrates graph neural networks, temporal behavioral clustering, causal inference over propagation paths, and multi-source feature fusion. We design a detection model grounded in behavioral temporal consistency, achieving 89.7% accuracy in identifying coordinated inauthentic accounts on a real-world Twitter dataset. Furthermore, we uncover three novel information manipulation topologies—previously unreported—revealing positional preferences of coordinated actors within propagation chains, characteristic delay patterns, and mechanisms by which coordination amplifies influence.
Technology Category
Application Category
Problem
Research questions and friction points this paper is trying to address.
Analyzing coordinated users' impact on Twitter information spread.
Identifying differences in behavior between coordinated and non-coordinated accounts.
Quantifying coordinated accounts' influence on cascade metrics and user interaction.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantify coordinated users' tactics efficacy
Introduce new measures for infectivity and interaction
Identify saturation-like interaction pattern threshold
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