Cyberbullying Detection Through Machine Learning: Can Technology Help to Prevent Internet Bullying?
Jacopo De Angelis1, Giulia Perasso2
1Jacopo De Angelis*, Department of Psychology, University of Milano-Bicocca, Milan, Italy.
2Giulia Perasso, Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
Manuscript received on July 07, 2020. | Revised Manuscript received on July 09, 2020. | Manuscript published on July 15, 2020. | PP: 57-69 | Volume-4 Issue-11, July 2020. | Retrieval Number: K10560741120/2020©BEIESP | DOI: 10.35940/ijmh.K1056.0741120
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© The Authors. Published By: 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: Cyberbullying constitutes a threat to adolescents’ psychosocial wellbeing that developed alongside technological progress. Detecting online bullying cases is still an issue because most of victims and bystanders do not timely report cyberbullying episodes to adults. Therefore, automatized technologies may play a critical role in detecting cyberbullying through the use of Machine Learning (ML). ML covers a broad range of techniques that enables systems to quickly access and learn from data, and to make decisions about complex problems. This contribution aims at deepening the role of ML in cyberbullying detection and prevention. Specifically, the following issues are addressed: i. identifying the features most frequently considered to develop ML models predicting cyberbullying; ii. identifying the most used ML algorithms and their evaluation methods; iii. understanding the implication of ML for prevention; iv. highlighting the main theoretical and methodological issues of ML algorithms in predicting cyberbullying. To answer these research questions, a systematic review of literature reviews, from a total of n=186 records from online databanks, has been conducted. Ten literature reviews have been elected to analyze and discuss evidence about ML preventative potential against cyberbullying. Most of the models used content-based features to predict cyberbullying. The majority of these features includes words written in social network posts, whereas Support Vector Machine, Naïve Bayes, and Convolutional Neural Networks are the most used alghorithms. Methodological and technical issues have been critically discussed. ML represents an innovative preventative strategy that may optimize and integrate educational programs for adolescents and be the starting point of the development of technology-based automatized detection strategies. Future research is challenged to develop algorithms capable of detecting cyberbullying from several multimedia sources.
Keywords: Cyberbullying; Machine Learning; Cyberbullying Detection; Systematic-Review; Prevention.