Uncovering coordinated cross-platform information operations: Threatening the integrity of the 2024 U.S. presidential election

Information operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital tracesof coordinated IOs on X (formerly Twitter). Using our machine learning framework for detecting online coordination, we analyze adataset comprising election-related conversations on X from May to July 2024. This reveals a network of coordinated inauthenticactors, displaying notable similarities in their link-sharing behaviors. Our analysis shows concerted efforts by these accounts todisseminate misleading, redundant, and biased information across the Web through a coordinated cross-platform information operation:The links shared by this network frequently direct users to other social media platforms or mock news sites featuring low-qualitypolitical content and, in turn, promoting the same X and YouTube accounts. Members of this network also shared deceptive imagesgenerated by AI, accompanied by language attacking political figures and symbolic imagery intended to convey power and dominance.While X has suspended or restricted a subset of these accounts, 75 percent of the coordinated network remains active, garneringsubstantial traction over time: The suspicious Web sites promoted by this coordinated network are shared thousands of times per day bythe X user base, further amplifying their reach and potential impact. Our findings underscore the critical role of developingcomputational models to scale up the detection of threats on large social media platforms, and emphasize the broader implications ofthese techniques to detect IOs across the wider Web.