Published Paper Details:

According to several suicide studies, there are about 800 000 suicides per year and it is still difficult to identify suicidal individuals. Social media usage has increased, and we have seen that users openly discuss their suicide attempts and plans on these platforms. This study attempts to prevent suicide by identifying suicidal profiles on social networks. First, we analyse social media profiles to extract various elements, such as account features connected to the profile and features related to social media data. Second, we present our approach for leveraging Twitter data to identify suicidal profiles based on machine learning techniques. Then, we make use of a profile data set made up of individuals who have already committed suicide. Experimental findings attest to the recall and accuracy efficacy of our technique in identifying suicidal characteristics. Finally, we demonstrate our research using a Python-based prototype that demonstrates the identification of suicidal characteristics.