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Identification of Antisocial Behavior Using Conventional Machine Learning and Deep Learning Algorithms

Volume 9 | Issue 2 | July 2024

     Your Paper Publication Details:

     Title: Identification of Antisocial Behavior Using Conventional Machine Learning and Deep Learning Algorithms

     DOI (Digital Object Identifier) :

     Pubished in Volume: 9  | Issue: 2  | Year: July 2024

     Publisher Name : IJSMER-Rems Publishing House | www.ejournal.rems.co.in | ISSN : 2455-6203

     Subject Area: Computer Science and Engineering, Machine Learning

     Author type: Indian Author

     Pubished in Volume: 9

     Issue: 2

     Pages: 1-9

     Year: July 2024

     E-ISSN Number: 2455-6203

     Download: 52

    Click Here to Download your Paper in PDF

     Abstract

    Online antisocial behavior encompasses actions that harm others in the digital realm. This can include cyberbullying, trolling, spreading misinformation, hacking as well as cyberbullying. People who might not normally participate in antisocial behavior in face-to-face conversations may feel more comfortable engaging in it when interacting online due to the anonymity and apparent distance. Addressing online antisocial behavior requires a combination of strategies, including education, awareness campaigns, and technological solutions. Platforms can implement features to mitigate harassment and abuse, while individuals can practice responsible online behavior and report inappropriate conduct.Detecting and classifying online antisocial behavior (ASB) can be challenging due to the diverse forms it can take and the nuancesof online interactions. However, several approaches can be used, Keyword-Based Filtering involves detecting specific keywords or phrases associated with ASB, such as threats, insults, or derogatory language. Labeled data can be used to train machine learning algorithms so they can automatically classify online content as ASB or non-ASB. These models can analyze text, images, or videos for patterns indicative of ASB. User BehaviorAnalyzer monitor user behavior for patterns associated with ASB, such as frequent use of inflammatory language or repeated interactions with known ASB accounts..

     Keywords

    Online antisocial behavior, deep learning, machine learning, Antisocial Online Behavior, Keyword-Based Filtering, User BehaviorAnalyzer.

     Authors and Affiliations

    Poonam Mathan
    Research Scholar,Computer Science & Engineering, Eklavya University Damoh, Madhya Pradesh,India
    Dr. Prof Anil Pimpalapure
    Professor & Dean, Computer Science & Engineering, Eklavya University Damoh, Madhya Pradesh,India, Email: gopisao0104@gmail.com
    Dr. Prashant Sen
    Associate Professor & HOD, Computer Science & Engineering, Eklavya University Damoh, Madhya Pradesh,India

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