  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.
