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Nowadays, the task of preventing suicide is one of the priorities in the health sector. Therefore, it is important to identify people prone to suicide at an early stage. This article discusses the possibility of real-time detection of visited websites containing suicidal statements. The classification of web pages is based on the analysis of the text
contained on it. This work can be divided into two parts: creating a browser extension and the server. The extension collects information about the content of the web pages visited by the user and transmits it to the server. The page classification process takes place on the server. In the final part of this work, a comparison of the effectiveness of detecting suicidal websites using various machine learning algorithms is presented. About 800 000 people commit suicide every year and detecting suicidal people remains a challenging issue as mentioned in a number of suicide studies. With the increased use of social media, we witnessed that people talk about their suicide plans or attempts in public on these networks. This paper addresses the problem of suicide prevention by
detecting suicidal profiles in social networks. First, we analyses profiles from social media and extract various features including account features that are related to the profile and features that are related to the social media data . Second, we introduce our method based on machine learning algorithms to detect suicidal profiles using Twitter data. Then, we use a profile data set consisting of people who have already committed suicide. Experimental results verify the effectiveness of our approaching terms of recall and precision to detect suicidal profiles. Finally, we present a Java based prototype of our work that shows the detection of suicidal profiles.

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