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Early risk detection of anorexia on social media
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UPF’s participation at the CLEF eRisk 2018: early risk prediction on the Internet
Ramíırez-Cifuentes, Diana; Freire, Ana. - : CEUR Workshop Proceedings
Abstract: This 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.
Keyword: Anorexia; Depression; Early risk detection; Machine learning; Social media
URL: http://hdl.handle.net/10230/35589
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Suicide risk assessment on social media: USI-UPF at the CLPsych 2019 shared task
Crestani, Fabio; Freire, Ana; Ríssola, Esteban A.. - : ACL (Association for Computational Linguistics)
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