Improving Time in the Extraction of Linguistic Patterns of Suicide on Twitter Using Swarm Intelligence

Suicide is the fourth leading cause of death for young people between 15 and 19 years of age, causing more than 703,000 deaths each year, corresponding to one death every 40 seconds, which is why it is considered a public health problem by the World Health Organization. One way to prevent suicide according to experts is to act quickly on those who are going through this situation. Twitter, like other social media platforms, is in an unique position to help, because of the cumulative feelings that are shared there, including suicidal thoughts and intentions. Given the increasing suicide rates in the world in this research we propose a model using swarm intelligence that allows us to reduce the pre-processing time of tweets in the detection of features related to suicide. The results presented prove that our model can be an alternative to reduce the time in the extraction of suicide-related linguistic patterns.