Ramíırez-Cifuentes, DianaFreire, Ana2018-10-092018-10-092018Ramírez-Cifuentes D, Freire A. UPF’s participation at the CLEF eRisk 2018: early risk prediction on the Internet. In. Cappellato L, Ferro N, Nie JY, Soulier L, editors. Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum; 2018 Sep 10-14; Avignon, France. [Avignon]: CEUR Workshop Proceedings; 2018. p. 1-12.1613-0073http://hdl.handle.net/10230/35589This paper describes the participation of the Web Science and Social Computing Research Group from the Universitat Pompeu Fabra, Barcelona (UPF) at CLEF 2018 eRisk Lab1. Its main goal, di- vided in two different tasks, is to detect, with enough anticipation, cases of depression (T1) and anorexia (T2) given a labeled dataset with texts written by social media users. Identifying depressed and anorexic indi- viduals by using automatic early detection methods, can provide experts a tool to do further research regarding these conditions, and help people living with them. Our proposal presents several machine learning models that rely on features based on linguistic information, domain-specific vo- cabulary and psychological processes. The results, regarding the F-Score, place our best models among the top 5 approaches for both tasks.application/pdfengCopyright © 2018 the authors.info:eu-repo/semantics/articleEarly risk detectionSocial mediaDepressionAnorexiaMachine learninginfo:eu-repo/semantics/openAccess