Social media serve as an alternate information source for patients, who use them to share information and provide social support. The aim of this research was to enable the analysis of patients’ tweets, by building a classifier of Twitter users that distinguishes patients from other entities. In the first stage of the research, a machine learning method, combining both social network analysis and natural language processing, was used to automatically classify users as patients or not. Three types of features were considered: (1) the user’s behavior on Twitter, (2) the content of the user’s tweets, and (3) the social structure of the user’s network. While different classification algorithms were considered, the best results (F1-score 0.808 and Precision 0.809) were achieved by a multiple-instance approach which constitute the novelty of this research. In the second stage of the research, the obtained classification methods were used to collect tweets of patients, in which they describe the different lifestyle changes they endure in order to deal with their disease. Using IBM Watson Service for entity sentiment analysis, frequently mentioned lifestyles were identified and their effectiveness on patients’ wellbeing was examined.
Stemmer, Maya; Ravid, Gilad; and Parmet, Yisrael, "A Framework for Identifying Patients on Twitter and Learning from Their Personal Experience" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
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