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

2024-10-30

arXiv.org

AI Summary

This study addresses the critical challenge of coordinated cross-platform disinformation campaigns—spanning X (formerly Twitter), Facebook, and Telegram—in the lead-up to the 2024 U.S. presidential election. We propose the first systematic framework for detecting coordinated manipulation across platforms, integrating multi-source data collection, graph neural network–based modeling of cross-platform similarity networks, behavioral temporal alignment, and anomaly detection to identify content reuse, synchronized dissemination patterns, and collaborative account behavior. Our analysis uncovers an organized content promotion pipeline linking Russian-affiliated media across Telegram and X. Empirically, we demonstrate that highly partisan, low-credibility, and conspiracy-themed content is systematically amplified through coordinated cross-platform communities. The framework delivers a rigorously validated analytical paradigm and empirically grounded evidence to support platform governance and countering information warfare.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• Detect cross-platform coordinated inauthentic activity
• Identify foreign interference in U.S. Election
• Uncover spread of partisan and low-credibility content

Innovation

• Cross-platform similarity networks
• Advanced coordination detection model
• Multi-platform data analysis