Unmasking information manipulation: A quantitative approach to detecting Copy-pasta, Rewording, and Translation on Social Media
Abstract: This study proposes a comprehensive methodology for identifying three techniques 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 these dimensions enables detection of abnormally close messages that are likely part of a coordinated campaign. We validate our approach using a synthetic dataset generated with ChatGPT and DeepL, further applying it to a real-world dataset on Venezuelan actors from Twitter Transparency. Our method successfully identifies all three types of inauthentic duplicates in the synthetic dataset, and is able to uncover inauthentic duplicates across political, commercial, and entertainment contexts in the Twitter dataset. The distinct focus on clustered alterations to messages, rather than individual messages, makes our approach efficient and effective at detecting large-scale instances of textual manipulation, including AI-generated ones. Moreover, our method offers a robust tool for identifying translated content, overlooked in previous research. This research also represents the first comprehensive analysis of copy-pasta detection, providing a reliable technique for tracking duplicate textual content across social networks.
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