Optimizing global processing time in the detection of patterns related to suicide in social networks

There is a suicide every 40 seconds in the world and it is the third cause of death for young people between 15 and 19 years old worldwide. For every suicide, many more attempt it, which is why suicide prevention remains an universal challenge and has been recognized by the World Health Organization (WHO) as a public health priority. Experts say that one of the best ways to prevent suicide is for people who are going through this urge to take their own lives to listen to people who are close to them and social networks such as Twitter or Facebook are in a unique position to help these people connect in real time in difficult situations that people with these suicidal tendencies are going through, but also represents a potential risk to receive information that could later prove harmful, either by stressing the same information or by taking some suicidal ideas. In this research we propose a model to optimize the global time processing in the detection of patterns related to suicide in the social network Twitter. Our results show that the proposed model can be a good alternative when it comes to optimizing the response time in this type of problems.