Examining the Feasibility of Off-the-Shelf Algorithms for Masking Directly Identifiable Information in Social Media Data
This addresses an understudied ethical and legal issue for social media data analysis, but it is incremental as it compiles existing algorithms rather than developing new ones.
The paper tackled the problem of removing directly identifiable information from social media data by evaluating off-the-shelf algorithms, resulting in the creation of a tool called Nightjar and an annotated dataset of tweets for this purpose.
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.