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Towards Suicide Prevention: Early Detection of Depression on Social Media - 2017

Towards Suicide Prevention: Early Detection Of Depression On Social Media

Research Area:  Machine Learning


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.


Author(s) Name:  Victor Leiva & Ana Freire

Journal name:  

Conferrence name:  Internet Science

Publisher name:  Springer

DOI:  10.1007/978-3-319-70284-1_34

Volume Information: