Approaches to automated detection of cyberbullying:A Survey

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. Rules-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 quality representative 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

DETECTING CYBERBULLYING IN ONLINE COMMUNITIES

Online communities are platforms enabling their users to interact over the web. In particular, they are popular among adolescents as a tool to discuss topics of mutual interest. However, offending commu-nication is a growing issue in these online environments. In its basic form, the process of sending mes-sages over electronic media to cause psychological damage to a victim is called online harassment. In a more severe form, cyberbullying is the process of sending offending messages several times to the same victim by the same offender. In this work, we propose an approach to detect cyberbullies and their victims. Identifying and aiding victims received only brief attention in existing work. We introduce a harassment graph to capture multiple message exchanges comprising cyberbullying cases. We show that our approach is able to precisely detect cyberbullies and their victims. Additionally, we propose metrics to measure the severity of online harassment and cyberbullying cases in terms of quantitative aspects. In particular, the metrics allow to identify victims of severe cyberbullying cases and might be used as an early indicator to provide fast and selective aid by administrators. We further propose use cases for our approach in online communities to tackle the problem of cyberbullying

Cyberbullying: youth's perceptions in a Johannesburg school context

A thesis submitted to the Faculty of Humanities, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Psychology, Johannesburg 2017Cyberbullying is a new form of bullying that has emerged as a by-product of interactive communication technologies, of which adolescents are the most prolific users. A meaningful number of adolescents are involved in cyberbullying and the impact can have a significant effect on the wellbeing of adolescents. The prevalence and the level of severity of this phenomenon is a matter of concern for society in general. Technology is pervasive and has become an integral part of the lives of adolescents; it can also leave individuals more vulnerable to harassment and violent behaviour penetration/victimisations. This study used Q methodology as an alternative approach to explore how South African adolescents’ perceived the nature of cyberbullying and its severity by providing insights into their subjective understanding of the phenomenon. A sample of 46 adolescents (aged 14 to 17 years of age) ranked two sets of statements (Q sort 1 and Q sort 2) that described cyberbullying behaviours and hypothetical cyberbullying events respectively. Participants sorted the statements according to personal significance within a fixed matrix. Their responses were analysed using the freeware statistical program PQ Method (Schmolck, 2014). A five-factor solution was identified and described for Q sort 1. The diversity of views emerging has implications for cyberbullying research, policy, and intervention and suggests different approaches for addressing this issue. Three distinct accounts of the severity of cyberbullying events emerged from the Q sort 2 analysis. These perspectives are discussed in relation to existing literature and the potential role of adults is considered. Participants also completed an open-ended questionnaire to inform their reactions to cyberbullying events by probing coping mechanisms. The distinct representations add to the understanding of this complex phenomenon.XL201

Children are Crying and Dying While the Supreme Court is Hiding: Why Public Schools Should Have Broad Authority to Regulate Off-Campus Bullying Speech

Bullying has long been a concern for students, parents, teachers, and school administrators. But technological advances—including the internet, cell phones, and social media—have transformed the nature of bullying and allow “cyberbullies” to extend their reach far beyond the schoolhouse gate. The U.S. Supreme Court established that schools may regulate on-campus speech if the speech creates a substantial disruption of, or material interference with, school activities. However, the Court has yet to rule on a school’s ability to regulate students’ off-campus bullying speech. This Note examines how various courts have approached the issue, analyzes the current circuit split, and ultimately proposes that schools should have the authority to discipline students for off-campus bullying speech

Detecting Online Harassment in Social Networks

Online Harassment is the process of sending messages over electronic media to cause psychological harm to a victim. In this paper, we propose a pattern-based approach to detect such messages. Since user generated texts contain noisy language, we perform a normalization step first to transform the words into their canonical forms. Additionally, we introduce a person identification module that marks phrases which relate to a person. Our results show that these preprocessing steps increase the classification performance. The pattern-based classifier uses the information provided by the preprocessing steps to detect patterns that connect a person to profane words. This technique achieves a substantial improvement compared to existing approaches. Finally, we discuss the portability of our approach to Social Networks and its possible contribution to tackle the abuse of such applications for the distribution of Online Harassment

Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability. In this paper, we further illustrate these issues, as we (i) evaluate many publicly available resources for this task and demonstrate difficulties with data collection. These predominantly yield small datasets that fail to capture the required complex social dynamics and impede direct comparison of progress. We (ii) conduct an extensive set of experiments that indicate a general lack of cross-domain generalization of classifiers trained on these sources, and openly provide this framework to replicate and extend our evaluation criteria. Finally, we (iii) present an effective crowdsourcing method: simulating real-life bullying scenarios in a lab setting generates plausible data that can be effectively used to enrich real data. This largely circumvents the restrictions on data that can be collected, and increases classifier performance. We believe these contributions can aid in improving the empirical practices of future research in the field

Bullying in a networked era: a literature review

This research update presents an aggregation and summary of recent academic literature on youth bullying. The purpose of this document is to “translate” scholarly research for a concerned public audience, which may include but is not limited to parents, caregivers, educators, and practitioners. This translation highlights recent findings and developments in the literature and makes them accessible to the informed but non-expert reader. The document’s two guiding questions are “What is bullying?” (Section I) and “What can be done about bullying?” (Section II). Section I begins with a brief overview of key definitions and related research conversations and then addresses bullying’s prevalence, the types of individuals involved in bullying, the characteristics of individuals involved and the consequences of their involvement, and community-level dynamics related to bullying. Section II covers four areas where action has been taken to address problems associated with bullying – school policy, curricula, school climate, and parents – and ends on a note about approaches that mix or cut across these areas. The purpose is to render lessons learned from research and assessment accessible to those interested in deepening or expanding their knowledge of bullying-related issues

The Structure of Cyber and Traditional Aggression: An Integrated Conceptualization

abstract: ABSTRACT The phenomenon of cyberbullying has captured the attention of educators and researchers alike as it has been associated with multiple aversive outcomes including suicide. Young people today have easy access to computer mediated communication (CMC) and frequently use it to harass one another -- a practice that many researchers have equated to cyberbullying. However, there is great disagreement among researchers whether intentional harmful actions carried out by way of CMC constitute cyberbullying, and some authors have argued that "cyber-aggression" is a more accurate term to describe this phenomenon. Disagreement in terms of cyberbullying's definition and methodological inconsistencies including choice of questionnaire items has resulted in highly variable results across cyberbullying studies. Researchers are in agreement however, that cyber and traditional forms of aggression are closely related phenomena, and have suggested that they may be extensions of one another. This research developed a comprehensive set of items to span cyber-aggression's content domain in order to 1) fully address all types of cyber-aggression, and 2) assess the interrelated nature of cyber and traditional aggression. These items were administered to 553 middle school students located in a central Illinois school district. Results from confirmatory factor analyses suggested that cyber-aggression is best conceptualized as integrated with traditional aggression, and that cyber and traditional aggression share two dimensions: direct-verbal and relational aggression. Additionally, results indicated that all forms of aggression are a function of general aggressive tendencies. This research identified two synthesized models combining cyber and traditional aggression into a shared framework that demonstrated excellent fit to the item data.Dissertation/ThesisPh.D. Educational Psychology 201