Identification and Classification of Cyberbully Incidents using Bystander Intervention Model
J.I. Sheeba1, S. Pradeep Devaneyan2, Revathy Cadiravane3
1J.I. Sheeba, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry (Tamil Nadu), India.
2S.Pradeep Devaneyan, Department of Mechanical and Building Sciences, Christ College of Engineering and Technology, Puducherry (Tamil Nadu), India.
3Revathy Cadiravane, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry (Tamil Nadu), India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 27 August 2019 | PP: 1-6 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10010782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1001.0782S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cyberharassment is bullying and degrading the adults by means of posting the comments like hurtful and derogatory humor over the internet in an online community. Though few bystanders ever try to reduce the conflicting effects of cyberbullying, and bystanders ever endeavor to interrupt. This will analyze the chattels of articulatory study on bystander intervention using the caricatured procedural made online Social Networking Sites. The proposed works mainly focus on the analysis of direct intervention by bystanders. The direct intervention allows bystanders to do reporting and blocking of cyberbully activities as additional features here. It will generate a report which contains the details of bully by means of alert message and block that bully by the bystander with the victim’s permission in the Facebook. This proposed framework will detect cyberbully words from the short hand text and emoticons on the comment sections using Latent semantic analysis (LSA). The Cyberbully words will be classified using a Random Decision Forest algorithm.
Keywords: Bystander Intervention, Cyberbullying Emoticons, Short Hand Text.
Scope of the Article: Classification