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.

中文翻译：

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检查用于屏蔽社交媒体数据中可直接识别信息的现成算法的可行性

尽管从道德和法律的角度来看是可取的，但从社交媒体数据中识别和删除/替换受保护的信息是一个研究不足的问题。本文确定了推文中包含的可能直接可识别的信息类型（受临床文本中受保护的健康信息的启发），这些信息可以使用现成的算法轻松删除，介绍了一个带有可识别信息注释的英文推文数据集，并编译这些-theshelf 算法集成到工具 (Nightjar) 中，以评估使用 Nightjar 从推文中删除直接可识别信息的可行性。Nightjar 以及带注释的数据可以从 https://bitbucket.org/mdredze/nightjar 中检索。