Improved cyberbully detection techniques using multiple correlation coefficient from forum corpus. (2018)

• Record Type: Journal Article
• Authors: Sheeba, J.I.; Devaneyan, S. Pradeep; Tata, Prathyusha
• Is Part Of: International journal of autonomic computing. Volume 3:Number 2(2018)
• Page Start: 152
• Page End: 171
• Publication Date: 2018
• Subjects: cyberbully detection -- cyberbully classification -- multiple correlation coefficients -- MCCs -- support vector machines -- SVMs -- data analytics -- DAs; Autonomic computing -- Periodicals; Computer science -- Periodicals; 004.05
• Languages: English
• ISSNs: 1741-8569

Abstract

Today, there are many prominent online sites where people share their experiences regarding crimes and anti-social behaviour. In this regard, a major unaddressed and even unidentified problem that is experienced in the social network websites is cyberbully . This proposed framework primarily targets the cyberbullying in the crime investigation forum since a high degree of cyberbully is common in crime forums. In this paper, a highly furnished representational framework is proposed that is specific to cyberbully detection using hybrid techniques (multiple correlation coefficient - MCC and support vector machine - SVM). The bag of words are given individual weights to examine their correlations using MCC algorithm before feeding them into a linear SVM classifier that identifies and classifies the cyberbully words. The efficiency of the system developed can be enhanced by analysing the evaluation metrics and the dataset validation metrics.