A Study of Self-Privacy Violations in Online Public Discourse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

User engagement in online public discourse often includes self-disclosure - the revelation of personal information. Such disclosures on online public platforms (e.g., news forums) become a shared history, vulnerable to detrimental use by advertisers and malicious parties. Yet, users indulge in self-disclosing behavior to attain strategic goals like relational development, social connectedness, identity clarification, and social control. In this work, we develop supervised models to detect instances of self-disclosure in users' comments in the context of public discourse. Using three different datasets, we validate the performance of our models. Our detection models achieve an accuracy of 75.8 percent in a news discourse dataset. The performances on evaluation against existing methods on two secondary datasets are on par if not better. We examine the rate at which users self-disclose to understand when and to what extent users abide by group norms of such behavior. Our results show that self-disclosing users are often similar in their alignment or divergence with the group norm. As such, these similarly divergent users in a conversation use similar language in their disclosures. Finally, we reflect o n t he i mplications o f alignment with or divergence from group norms in light of online privacy.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1041-1050
Number of pages10
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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