Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

Nomandla Mkwananzi, Hanlie Smuts
Copyright: © 2023 |Pages: 13
DOI: 10.4018/IJSMOC.330533
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Abstract

The intensive use of the internet comes with negative and positive effects. Cyberbullying is one of the negative effects of using the internet. Cyberbullying has a negative effect on the victims emotionally, academically, and psychologically. Cyberbullying detection tools can help in reducing or eliminating cyberbullying on social media platforms. The aim of the study was to identify the elements that drive cyberbullying and build classification models to determine whether social media textual information contains cyberbullying text or not. The research aim was achieved through a mixed methods research design, containing qualitative and quantitative elements. The drivers of cyberbullying were identified through a literature review. These included age, gender, family structure, parental education, race, technology, anonymity, academic achievement, and awareness of cyber safety. The support vector machines and naïve Bayes models were used to classify the text dataset (Formspring.me dataset), with a 72.81% and a 99.87% classification accuracy, respectively.
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Background

Cyberbullying is a term that was defined by Bill Besley, a Canadian educator (Macaulay et al., 2018). Besley (2005), states that cyberbullying mainly involves the use of technology to make deliberate attacks; it involves persistent bad behaviour to harm others. The bullies inflict pain on other individuals or a group of people by using technology.

The participant roles include the victim, the bully, the assistants, the reinforcers of the bully, the defenders of the victim, and the outsiders who watch from a distance (Hee et al., 2018). Intention, repetition and power imbalance are three criteria that describe bullying. Intention means that the bully deliberately hurts the victim; repetition refers to the frequency of the bullying instances, and power imbalance refers to the vulnerability of the victim of bullying – the bully is more powerful than the victim (Hee et al., 2018). Technological skills, anonymity and failure of the victim to escape the bully determine the online interaction power over the victim (Görzig & Ólafsson, 2012; Hee et al., 2018).

Cyberbullying policies are enforced through social media companies’ policies to address cyberbullying incidents on their platforms, through use of software, human or automated systems and geofencing (Milosevic, 2016). Violation of these policies leads to blocking of the user, removal of the content from the platform, and in some circumstances the case is reported to the relevant authorities (Milosevic, 2016).

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