Towards suicide prevention: Early detection of depression on social media

  • Authors
  • Leiva, Víctor; Freire, Ana
  • UPF authors
  • FREIRE VEIGA, ANA MARIA;
  • Type
  • Scholarly articles
  • Journal títle
  • Lecture Notes in Computer Science / Artificial Intelligence
  • Publication year
  • 2017
  • Volume
  • 10673
  • Pages
  • 428-436
  • ISSN
  • 0302-9743
  • Publication State
  • Published
  • 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.
  • Complete citation
  • Leiva, Víctor; Freire, Ana. Towards suicide prevention: Early detection of depression on social media. Lecture Notes in Computer Science / Artificial Intelligence 2017; 10673( ): 428-436.
Bibliometric indicators
  • 19 times cited Scopus
  • 13 times cited WOS
  • Índex Scimago de 0.295 (2017)