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Towards Suicide Prevention: Early Detection of Depression on Social Media

  • Victor Leiva
  • Ana FreireEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10673)

Abstract

The statistics presented by the World Health Organization inform that 90% of the suicides can be attributed to mental illnesses in high-income countries. Besides, previous studies concluded that people with mental illnesses tend to reveal their mental condition on social media, as a way of relief. Thus, the main objective of this work is the analysis of the messages that a user posts online, sequentially through a time period, and detect as soon as possible if this user is at risk of depression. This paper is a preliminary attempt to minimize measures that penalize the delay in detecting positive cases. Our experiments underline the importance of an exhaustive sentiment analysis and a combination of learning algorithms to detect early symptoms of depression.

Keywords

Early detection Depression Social media 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 International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Communications and Information TechnologiesUniversitat Pompeu FabraBarcelonaSpain

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