Suicide is a person's deliberate act of ending his/her own life. Suicide reveals serious personal problems but also often reflects a deterioration of the social context in which an individual lives. According to a recent and alarming WHO (World Health Organisation) report (September 4, 2014), one person dies of suicide every 40 seconds in the world - more than all the yearly victims of wars and natural disaster – more than 1,100,000 by year. Most suicide attempts are supported by hospital emergency units. Suicide is a major public health issue with strong socio-economic consequences. For example, the economic cost of suicide was estimated to 5 billion euros in 2009 in France. In the framework of the 2013-2020 Mental Health Action Plan, WHO member states plan 10% reduction in suicide rates in each country before 2020.
The main objective is to design and implement new approaches for early detection of at risk individuals through their use of social media. We have already developed predictive prototypes based on attentive deep models, which has proven to be effective for identifying at-risk individuals of the most common mental disorder (depression, signs of anorexia and self-harm) through modeling the temporal variation and capture a possible deterioration in users mental state in order to offer assistance when needed.
We would like to acknowledge La Région Occitanie and lAgglomération Béziers Méditerranée which finance this thesis as well as INSERM and CNRS for their financial support of CONTROV project.