Abstract

Nowadays, false and unverified information on social media sway individuals’ perceptions during major geo-political events and threaten the quality of the whole digital information ecosystem. Since the Russian invasion of Ukraine, several fact-checking organizations have been actively involved in verifying stories related to the conflict that circulated online. In this paper, we leverage a public repository of fact-checked claims to build a methodological framework for automatically identifying false and unsubstantiated claims spreading on Twitter in February 2022. Our framework consists of two sequential models: First, the claim detection model identifies whether tweets incorporate a (false) claim among those considered in our collection. Then, the claim retrieval model matches the tweets with fact-checked information by ranking verified claims according to their relevance with the input tweet. Both models are based on …

Date

January 1, 1970

Authors

Valerio La Gatta, Chiyu Wei, Luca Luceri, Francesco Pierri, Emilio Ferrara

Conference

WWW '23: Companion Proceedings of the ACM Web Conference 2023

Pages

1317-1323

Publisher

ACM