Suicide has long been a worrisome problem for society and is an event that has far-reaching effects. Health organizations such as the World Health Organization (WHO) and the French National Observatory of Suicide (ONS) have pledged to reduce the number of suicides by 10% in all countries by 2020. While suicide is a very marked event, there are often behaviors and words that can act as early signs of predisposition to suicide. The objective of this internship is to develop a system that semi-automatically detects these markers through social networks. Previous work has proposed the classification of Tweets using vocabulary in topics related to suicide: sadness, psychological injuries, mental state, depression, fear, loneliness, proposed suicide method, anorexia, insults, and cyber bullying. During this training period, we add a new dimension, time to reflect changes in the status of monitored people. We implemented it with different learning methods including an original concept drift method. We have successfully experienced this method on synthetic and real data sets issued from the Facebook networks.