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

Hadj Ahmed Bouarara
Additional contact information
Hadj Ahmed Bouarara: GeCoDe Laboratory, Saida, Algeria

International Journal of Knowledge-Based Organizations (IJKBO), 2020, vol. 10, issue 1, 49-61

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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJKBO.2020010103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jkbo00:v:10:y:2020:i:1:p:49-61

Access Statistics for this article

International Journal of Knowledge-Based Organizations (IJKBO) is currently edited by John Wang

More articles in International Journal of Knowledge-Based Organizations (IJKBO) from IGI Global
Bibliographic data for series maintained by Journal Editor ().