RT Journal Article T1 A framework to extract biomedical knowledge from gluten-related tweets: the case of dietary concerns in digital era A1 Perez Perez, Martín A1 Igrejas, Gilberto A1 Fernández Riverola, Florentino A1 GARCIA LOURENÇO, Analia Maria K1 1203.12 Bancos de Datos AB Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from >400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions. PB Artificial Intelligence in Medicine SN 09333657 YR 2021 FD 2021-08 LK http://hdl.handle.net/11093/2307 UL http://hdl.handle.net/11093/2307 LA eng NO Artificial Intelligence in Medicine, 118: 102131 (2021) NO Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG NO Fundação para a Ciência e a Tecnologia | Ref. UIDB/50006/2020 DS Investigo RD 09-oct-2024