Approaches to Automated Detection of Cyberbullying: A Survey

Abstract

Abstract-Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field.

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

“…The electronic form of traditional bullying is called cyberbullying. which is the aggression and harassment that is targeted to an individual who is unable to defend himself [38]. Bullying is known with its repetitive act to the same individual, unlike hate speech which is more general and not necessarily intended to hurt a specific individual.…”

Privacy Preserving Cyberbullying Prevention with AI Methods in 5G Networks

“…PROBLEM STATEMENT As we explained before, cyberbullying can have a lifetime bad psychological effects on its victims as well as on the offenders [18], [19]. Moreover, automatic detection of a cyberbullying incident is difficult because the nature of bullying is subjective [8].…”

“…1) Direct : The presence of cyberbullying keywords (threat, curse, racism, hate and sexism) in online texts is the easiest way to detect a cyberbullying incident, because such message is intended to harm its receiver, and therefore, most probably contains harassment and abusive keywords [8], [20].…”

“…Although, there have been several studies on how to detect cyberbullying with the help of Artificial Intelligence (AI) methods [7], [8], it is still difficult to instantly detect a harmful electronic context [7], and protect victim's of cyberbullying to receive such context. On the other hand, individual's messages and online comments are considered to be private.…”

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