Exposing Cross-Platform Coordinated Inauthentic Activity in the Run-Up to the 2024 U.S. Election

TL;DR

The paper tackles cross-platform coordinated inauthentic activity in the run-up to the 2024 U.S. election. It introduces a multi-platform dataset spanning $\mathbb{X}$, Facebook, and Telegram and an unsupervised, network-based framework that detects intra- and cross-platform CoIA via co-URL similarity networks and content similarity networks. The study reveals networks promoting Russian state media across Telegram and $\mathbb{X}$ and details the topics, credibility signals, and AI-generated content used, highlighting the scale and cross-platform reach of influence campaigns. The results motivate cross-platform regulatory approaches to curb coordinated influence that transcends single platforms.

Abstract

Coordinated information operations remain a persistent challenge on social media, despite platform efforts to curb them. While previous research has primarily focused on identifying these operations within individual platforms, this study shows that coordination frequently transcends platform boundaries. Leveraging newly collected data of online conversations related to the 2024 U.S. Election across $\mathbb{X}$ (formerly, Twitter), Facebook, and Telegram, we construct similarity networks to detect coordinated communities exhibiting suspicious sharing behaviors within and across platforms. Proposing an advanced coordination detection model, we reveal evidence of potential foreign interference, with Russian-affiliated media being systematically promoted across Telegram and $\mathbb{X}$. Our analysis also uncovers substantial intra- and cross-platform coordinated inauthentic activity, driving the spread of highly partisan, low-credibility, and conspiratorial content. These findings highlight the urgent need for regulatory measures that extend beyond individual platforms to effectively address the growing challenge of cross-platform coordinated influence campaigns.

Figures (6)

• Figure 1: Cross-platform coordination network illustrating user coordination across the three social media platforms. The percentages at the bottom right indicate the proportion of users from each platform within this network.
• Figure 2: Content analysis of coordinated accounts on Telegram, $\mathbb{X}$, and Facebook. We report the top-10 most recurring topics and compare their prevalence against the organic discourse.
• Figure 3: Empirical Cumulative Distribution of total engagement, defined as the sum of user interactions across platforms, including reactions, comments, shares, replies, and views—generated by coordinated and organic users.
• Figure 4: AIGC distribution across the three platforms, highlighting the prevalence of AI-generated posts among coordinated and organic users.
• Figure C.1: Thresholds grid search for filtering similarity graph: x-axis edge similarity quantile, y-axis node centrality quantile. z-axis is the minimum graph density across all connected components of the filtered co-url similarity graph. The green square corresponds to the selected thresholds.