Authorship Attribution in Health Forums

Proceedings of Recent Advances in Natural Language Processing, pages 74–82, Hissar, Bulgaria, 7-13 September 2013. Authorship Attribution in Health Forums Victoria Bobicev Technical University of Moldova Chisinau, Moldova vika@rol.md Marina Sokolova CHEO Research Institute, Ottawa University of Ottawa, Ontario, Canada sokolova@uottawa.ca Khaled El Emam CHEO Research Institute, Ottawa University of Ottawa, Ontario, Canada kelemam@uottawa.ca Stan Matwin Dalhousie University, Halifax, Canada Polish Academy of Sciences, Warsaw, Poland stan@cs.dal.ca Abstract The emergence of social media (networks, blogs, web forums) has given people numer- ous opportunities to share their personal sto- ries, including details of their health. Although users mostly post under assumed nicknames, state-of-the-art text analysis techniques can combine texts from different media and use that linkage to identify private details of an in- dividual‟s health. In this study we aim to em- pirically examine the accuracy of identifying authors of on-line posts on a medical forum.1 Our results show a high accuracy of the au- thorship attribution, especially when text is represented by the orthographical features. 1 Introduction Emergence of social media (networks, blogs, web forums) has given people numerous oppor- tunities to share their personal stories, including details of their health (e.g., disease diagnosis, symptoms, treatment) (Velden and Emam, 2012; Bobicev et at, 2012):  The transfer went well - my RE did it himself which was comforting. 2 embies (grade 1 but slow in development) so I am not holding my breath for a positive.  I've had 7 IUI and one ivf all cancelled due to not ovulating. I am a poor responder. What 1 This work had been done when the first author was a visit- ing professor at CHEO Research Institute. bothers me the most is never getting to the point of actually going thru the procedure.2 Sharing personal health information (PHI) is a behavior that can be seen in 80% of Internet us- ers, or in 59% of all adults, who reported search- ing for health information (Fox, 2011). Although users mostly post under assumed nicknames, state-of-the-art text analysis tech- niques can combine texts from different forums and then use that linkage to identify private de- tails of an individual‟s health. Aggregating and mining posts from five forums, Li et al. (2011) identified the user‟s full name, date of birth, spouse‟s name, home address, home phone num- ber, cell phone number, email, occupation and the lab test results. The latter are highly indica- tive of the suspected disease, and hence, of the health conditions of the said individual. In order to gauge how best to protect internet user anonymity, we first wanted to know the ability of Text Mining techniques in authorship attribution on medical forums, i.e. the task of identification of an author among other authors posting on the same forum. The attribution is based on comparison of a new text to texts pre- viously written by known authors. We obtained the empirical evidence on the posts from an on-line community of IVF (In Vi- tro Fertilization) patients. We achieved a highly accurate authorship …

Content truncated to 500 words