2020/11/16 by Rachel Dorn, Dorn, Rachel, Alicia L. Nobles +5
Computer Science · Medicine · Social Sciences · #Ethics in Clinical Research #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Misinformation and Its Impacts #Privacy, Security, and Data Protection #cs.HC
paper · pdf · doi:10.48550/arxiv.2011.08324
arxiv created 2020/11/16 · openalex publication_date 2020/11/16 · arxiv updated 2020/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The identification and removal/replacement of protected information from social media data is an understudied problem, despite being desirable from an ethical and legal perspective. This paper identifies types of potentially directly identifiable information (inspired by protected health information in clinical texts) contained in tweets that may be readily removed using off-the-shelf algorithms, introduces an English dataset of tweets annotated for identifiable information, and compiles these off-the-shelf algorithms into a tool (Nightjar) to evaluate the feasibility of using Nightjar to remove directly identifiable information from the tweets. Nightjar as well as the annotated data can be retrieved from https://bitbucket.org/mdredze/nightjar.