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In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named\n            <jats:bold>Generative Text Compression with Agglomerative Clustering Summarization<\/jats:bold>\n            (\n            <jats:bold>GTCACS<\/jats:bold>\n            ), and an in-depth data analytic process. We advance the state of the art on automated detection of\n            <jats:bold>adverse drug reactions<\/jats:bold>\n            (\n            <jats:bold>ADRs<\/jats:bold>\n            ) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.\n          <\/jats:p>","DOI":"10.1145\/3468781","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T01:39:53Z","timestamp":1634434793000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism"],"prefix":"10.1145","volume":"3","author":[{"given":"Giorgio","family":"Grani","sequence":"first","affiliation":[{"name":"Sapienza University of Rome, Rome (RM), Italy"}]},{"given":"Andrea","family":"Lenzi","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome, Rome (RM), Italy"}]},{"given":"Paola","family":"Velardi","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome, Rome (RM), Italy"}]}],"member":"320","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"339","DOI":"10.18653\/v1\/W19-5036","volume-title":"Proceedings of the 18th BioNLP Workshop and Shared Task","author":"Alhuzali Hassan","year":"2019","unstructured":"Hassan Alhuzali and Sophia Ananiadou. 2019. Improving classification of adverse drug reactions through using sentiment analysis and transfer learning. In Proceedings of the 18th BioNLP Workshop and Shared Task. Association for Computational Linguistics, 339\u2013347. https:\/\/doi.org\/10.18653\/v1\/W19-5036"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3006299.3006335"},{"key":"e_1_3_3_4_2","volume-title":"Proceedings of the 2017 Text Analysis Conference","author":"Belousov Maksim","year":"2017","unstructured":"Maksim Belousov, Nikola Milosevic, William Dixon, and Goran Nenadic. 2017. Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017. In Proceedings of the 2017 Text Analysis Conference (TAC\u201917). 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