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This study proposes a comprehensive methodology for identifying threetechniques utilized in foreign-operated information manipulation campaigns:Copy-Pasta, Rewording, and Translation. Our approach, dubbed the``$3\Delta$-space duplicate methodology'', quantifies the semantic, grapheme,and language aspects of messages. Computing pairwise distances within thesedimensions enables detection of abnormally close messages that are likely partof a coordinated campaign. We validate our approach using a synthetic datasetgenerated with ChatGPT and DeepL, further applying it to a real-world dataseton Venezuelan actors from Twitter Transparency. Our method successfullyidentifies all three types of inauthentic duplicates in the synthetic dataset,and is able to uncover inauthentic duplicates across political, commercial, andentertainment contexts in the Twitter dataset. The distinct focus on clusteredalterations to messages, rather than individual messages, makes our approachefficient and effective at detecting large-scale instances of textualmanipulation, including AI-generated ones. Moreover, our method offers a robusttool for identifying translated content, overlooked in previous research. Thisresearch also represents the first comprehensive analysis of copy-pastadetection, providing a reliable technique for tracking duplicate textualcontent across social networks.

Unmasking information manipulation: A quantitative approach to detecting Copy-pasta, Rewording, and Translation on Social Media