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Early Risk Detection of Anorexia on Social Media

  • Diana Ramírez-Cifuentes
  • Marc Mayans
  • Ana FreireEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11193)

Abstract

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.

Keywords

Early risk detection Eating disorders Social media Anorexia Machine learning 

Notes

Acknowledgements

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Diana Ramírez-Cifuentes
    • 1
  • Marc Mayans
    • 1
  • Ana Freire
    • 1
    Email author
  1. 1.Web Science and Social Computing Research GroupUniversitat Pompeu Fabra, BarcelonaBarcelonaSpain

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