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Detection and Prevention of Twitter Users with Suicidal Self-Harm Behavior

Detection and Prevention of Twitter Users with Suicidal Self-Harm Behavior
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Author(s): Hadj Ahmed Bouarara (GeCoDe Laboratory, Saida, Algeria)
Copyright: 2020
Volume: 10
Issue: 1
Pages: 13
Source title: International Journal of Knowledge-Based Organizations (IJKBO)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 10.4018/IJKBO.2020010103

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Abstract

Recently, with the development of communication means such as 4G and the rapid growth of the use of mobile devices (smartphones and tablets) the number of twitter users has increased exponentially. By the end of 2018 Twitter had 321 million active users with over 600 million tweets every day. However, all this information will have no use if we cannot access the meaning it carries. The authors' idea is to identify Twitter users with suicidal or self-harm behaviors by analyzing their tweets using an algorithm inspired from the social life of Asian elephants. The objective is to prevent the situations of depressions, threats of suicide or any other form of self-destructive behavior that exists on Twitter.

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