Measuring the impact of coordinated accounts on social media influence
Optimal and heuristic strategies for evaluating the influence of coordinated behavior in information cascades and retweet networks
Social and Information NetworksComputers and Society
Summary
Coordinated groups on social media can spread information together, but it's unclear how much influence they really have. The authors created two ways to measure how much these groups can affect the spread of information, using Twitter data from elections and known disinformation campaigns. They found that in some cases, coordinated accounts have limited impact, while in others they can be as influential as very central users in the network. Their two measurement methods agreed well, suggesting these findings reflect real network features, not just measurement quirks.
What this means in practice
- •For social media analysts: Assess the potential impact of coordinated inauthentic accounts on online information spread using network and cascade analysis.
- •For cybersecurity teams: Prioritize monitoring or countermeasures by estimating how much coordinated behavior boosts influence in social networks during political events.
Authors
Niccolò Di Marco, Matteo Cinelli, Shinichi Nakano, Andrea Frosini
Abstract
Coordinated Inauthentic Behavior (CIB) has become a major concern in online social platforms, yet its actual impact on information diffusion remains poorly understood. Existing research has primarily focused on detecting coordinated activity, while comparatively little attention has been devoted to quantifying its influence once detected. In this work, we introduce two complementary frameworks for the post-hoc evaluation of coordinated accounts. First, we formulate the problem on information cascades as a constrained influence maximization problem over directed trees and develop a polynomial-time dynamic programming algorithm that computes the optimal placement of coordinated nodes, providing an upper bound on their achievable influence. Second, motivated by the limited availability of diffusion cascades in real-world platforms, we propose a network-based framework that estimates influence directly from retweet networks using the independent cascade model and compares the observed placement of coordinated accounts against established heuristic baselines. We evaluate both approaches on Twitter/X data from the 2019 UK General Election and on a collection of verified state-backed information operation campaigns spanning multiple countries. While coordinated accounts exhibit limited influence in the UK cascades, the network-based analysis reveals substantial differences across campaigns, with several operations achieving influence comparable to or exceeding that of structurally central seed sets. Finally, by reconstructing cascades from the retweet networks, we show that the two frameworks produce consistent results, suggesting that the observed effects reflect intrinsic structural properties of coordinated activity rather than artifacts of the underlying methodology.