Singh, Ravinder, Subramani, Sudha ORCID: 0000-0002-8102-0278, Du, Jiahua, Zhang, Yanchun ORCID: 0000-0002-5094-5980, Wang, Hua ORCID: 0000-0002-8465-0996, Miao, Yuan ORCID: 0000-0002-6712-3465 and Ahmed, Khandakar ORCID: 0000-0003-1043-2029 (2023) Antisocial behavior identification from Twitter feeds using traditional machine learning algorithms and deep learning. ICST Transactions on Scalable Information Systems, 10 (4). ISSN 2032-9407

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.

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