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      Automated Detection of Doxing on Twitter

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          Abstract

          Doxing refers to the practice of disclosing sensitive personal information about a person without their consent. This form of cyberbullying is an unpleasant and sometimes dangerous phenomenon for online social networks. Although prior work exists on automated identification of other types of cyberbullying, a need exists for methods capable of detecting doxing on Twitter specifically. We propose and evaluate a set of approaches for automatically detecting second- and third-party disclosures on Twitter of sensitive private information, a subset of which constitutes doxing. We summarize our findings of common intentions behind doxing episodes and compare nine different approaches for automated detection based on string-matching and one-hot encoded heuristics, as well as word and contextualized string embedding representations of tweets. We identify an approach providing 96.86% accuracy and 97.37% recall using contextualized string embeddings and conclude by discussing the practicality of our proposed methods.

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          Most cited references51

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          Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

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            Deep Learning for Hate Speech Detection in Tweets

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              Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network

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                Author and article information

                Journal
                Proceedings of the ACM on Human-Computer Interaction
                Proc. ACM Hum.-Comput. Interact.
                Association for Computing Machinery (ACM)
                2573-0142
                November 07 2022
                November 11 2022
                November 07 2022
                : 6
                : CSCW2
                : 1-24
                Affiliations
                [1 ]Pennsylvania State University, State College, PA, USA
                Article
                10.1145/3555167
                219735e8-f556-415d-ad2e-7b050f3db16c
                © 2022
                History

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