Research Article
Antisocial Behavior Identification from Twitter Feeds Using Traditional Machine Learning Algorithms and Deep Learning.
@ARTICLE{10.4108/eetsis.v10i3.3184, author={Ravinder Singh and Sudha Subramani and Jiahua Du and Yanchun Zhang and Hua Wang and Yuan Miao and Khandakar Ahmed}, title={Antisocial Behavior Identification from Twitter Feeds Using Traditional Machine Learning Algorithms and Deep Learning.}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={4}, publisher={EAI}, journal_a={SIS}, year={2023}, month={5}, keywords={Antisocial Behavior Disorder, Behavior Classification, Personality Disorder, Online Antisocial Behavior, Deep Learning, Machine Learning}, doi={10.4108/eetsis.v10i3.3184} }- Ravinder Singh
Sudha Subramani
Jiahua Du
Yanchun Zhang
Hua Wang
Yuan Miao
Khandakar Ahmed
Year: 2023
Antisocial Behavior Identification from Twitter Feeds Using Traditional Machine Learning Algorithms and Deep Learning.
SIS
EAI
DOI: 10.4108/eetsis.v10i3.3184
Abstract
Antisocial behavior (ASB) is one of the ten personality disorders included in ‘The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and falls in the same cluster as Borderline Personality Disorder, Histrionic Personality Disorder, and Narcissistic Personality Disorder. It is a prevalent pattern of disregard for and violation of the rights of others. Online antisocial behavior is a social problem and a public health threat. An act of ASB might be fun for a perpetrator; however, it can drive a victim into depression, self-confinement, low self-esteem, anxiety, anger, and suicidal ideation. Online platforms such as Twitter and Reddit can sometimes become breeding grounds for such behavior by allowing people suffering from ASB disorder to manifest their behavior online freely. In this paper, we propose a proactive approach based on natural language processing and deep learning that can enable online platforms to actively look for the signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can prevent dire situations leading to someone committing suicide.
Copyright © 2023 Ravinder Singh et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
