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Early risk detection of anorexia on social media
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Abstract:
Comunicació presentada a: INSCI 2018 celebrada del 24 al 26 d'octubre de 2018 a Sant Petersburg, Rússia. ; This paper proposes an approach for the early detection of anorexia nervosa (AN) on social media. We present a machine learning approach that processes the texts written by social media users. This method relies on a set of features based on domain-specific vocabulary, topics, psychological processes, and linguistic information extracted from the users’ writings. This approach penalizes the delay in detecting positive cases in order to classify the users in risk as early as possible. Identifying anorexia early, along with an appropriate treatment, improves the speed of recovery and the likelihood of staying free of the illness. The results of this work showed that our proposal is suitable for the early detection of AN symptoms. ; This work was supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
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Keyword:
Anorexia; Early risk detection; Eating disorders; Machine learning; Social media
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URL: http://hdl.handle.net/10230/36745
https://doi.org/10.1007/978-3-030-01437-7_1
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UPF’s participation at the CLEF eRisk 2018: early risk prediction on the Internet
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Suicide risk assessment on social media: USI-UPF at the CLPsych 2019 shared task
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