Research Summary: Optimal and heuristic strategies for evaluating the influence of coordinated behavior in information cascades and retweet networks

This research addresses a critical gap in understanding the actual impact of Coordinated Inauthentic Behavior (CIB) on information diffusion within online social platforms. While previous efforts focused on CIB detection, this work introduces novel frameworks to quantify its influence post-detection, specifically by formulating it as a constrained influence maximization problem and developing algorithms to assess the optimal placement and maximum achievable influence of coordinated nodes in information cascades.

Why it matters

Quantifying the influence of coordinated behavior on information spread is crucial for understanding and mitigating its effects on public discourse and organizational reputation. This research provides advanced analytical tools that enable a more precise assessment of threats posed by sophisticated manipulation tactics, informing strategic responses to protect informational integrity and trust in digital ecosystems.

Key insights

• Existing research predominantly focuses on detecting Coordinated Inauthentic Behavior (CIB) rather than quantifying its actual impact on information diffusion.
• A new framework formulates the problem of evaluating CIB influence on information cascades as a constrained influence maximization problem over directed trees.
• A polynomial-time dynamic programming algorithm has been developed to compute the optimal placement of coordinated nodes, establishing an upper bound on their potential influence.
• The research provides methods for post-hoc evaluation of coordinated accounts, moving beyond mere detection to impact assessment.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.24398