It started in the darkest corners of the internet. Through late August and early September 2024, neo-Nazis on Gab were seeding a fabricated story: Haitian immigrants in Springfield, Ohio, were eating the local pets. The claim went mainstream when a resident posted a secondhand version to a private Facebook group — something she’d heard from her neighbor’s daughter’s friend.
From there, the lie jumped to X and Truth Social. Soon, it even made its way to cable news. By the time then-presidential candidate Donald Trump repeated it at the presidential debate on Sept. 10 — before an audience of 67 million — the claim had already spawned AI-generated memes and campaign billboards. Thirty-three bomb threats followed; schools, hospitals and government buildings were evacuated.
Imagine if a fact-checker had known it was coming. Not after the damage was done, but days before it crossed from the fringes to the mainstream.
A team of USC researchers has built a system that can predict when a false rumor will jump from one social media platform to another, days before it goes mainstream.
Their paper, “Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks,” accepted at The Web Conference 2026, offers something the field has long lacked: a way to see across platform boundaries before a false narrative spreads. The work was led by Patrick Gerard, a USC Viterbi Ph.D. student in computer science at the USC Information Sciences Institute, and co-authored by Luca Luceri, a research assistant professor at the USC Thomas Lord Department of Computer Science and lead scientist at ISI, both within the USC Mark and Mary Stevens School of Computing and Artificial Intelligence; Leonardo Blas, a USC Viterbi Ph.D. student advised by Emilio Ferrara; and Ferrara, a principal scientist at ISI and USC Viterbi professor of computer science.
