Manipulating Twitter Through Deletions

• URL: http://arxiv.org/abs/2203.13893v1
• Date: Fri, 25 Mar 2022 20:07:08 GMT
• Title: Manipulating Twitter Through Deletions
• Authors: Christopher Torres-Lugo, Manita Pote, Alexander Nwala, Filippo Menczer
• Abstract summary: Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. We find that a small fraction of accounts delete a large number of tweets daily. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms.
• Score: 64.33261764633504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. These APIs provide access to public tweets that have not been deleted. However, bad actors can delete content strategically to manipulate the system. Unfortunately, estimates based on publicly available Twitter data underestimate the true deletion volume. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. We find that a small fraction of accounts delete a large number of tweets daily. We also uncover two abusive behaviors that exploit deletions. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms. These kinds of abuse can be exploited to amplify content and inflate popularity, while evading detection. Our study provides platforms and researchers with new methods for identifying social media abuse.

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