Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies

Tackling bullying with technology:a literature review of existing bullying prevention solutions

Abstract. Bullying is a serious problem that affects people all around the world, particularly children. The consequences of bullying are so severe that the issue cannot be set aside. There are multiple ways to tackle bullying, and in recent years technology has been brought up as a possible solution. There are different kinds of technological solutions that have different points-of-view to the issue and on how to solve it. The research method applied in this thesis was a literature review of the technological solutions that have been developed to battle bullying. The purpose of the study was to examine and describe the existing bullying prevention technologies and their distinctive features. Two databases were used to gain material for this study, and through very strict exclusion criteria and several analyses from over 2000 search results, 15 articles were included in this study. Bullying and cyberbullying as concepts are explained. The study results pointed out four groups of possible solutions to bullying: Serious Games, anti-bullying apps, bullying detecting algorithms, and solutions that combine more than one type of technology as well as one group for cyberbullying. These groups are presented with suitable examples and their identified distinctive characteristics

Vulnerability to radicalisation in a general population: a psychometric network approach

A public health approach to countering the threat from extremism aims to manage vulnerability before behaviour escalates to require involvement from the criminal justice system. Fundamental to applying a public health approach is understanding how risk (and protective) factors can be modified, in other words, the functional roles of these factors. To unpack the functional roles of risk factors, a more dynamic approach to modelling the complex relationships between factors is needed. In the present study we surveyed a representative sample of the UK general population (n = 1500) where participants self-reported risk factors and indicators for vulnerability to radicalisation. Operationalising analytical guidance from a Risk Analysis Framework (RAF), we applied psychometric network modelling to visualise the relationships among risk factors relating to individual-level propensities, situational influences, and exposure to extremism-enabling environments. We present our results as a series of network graphs and discuss (a) how risk factors ‘cluster’ or ‘co-occur’, (b) the most influential risk factors which may be important for intervention and prevention, and (c) ‘risk pathways’ which suggest potential putative risk and/or protective factors. We present our findings as evidence for a public health approach to countering the threat from extremism

A Quasi-Experimental Analysis Of School-Based Situational Crime Prevention Measures

In recent years, there has been an expansion of situational crime prevention (SCP) measures in K-12 schools, including physical controls, law enforcement personnel, and security policies that are designed to prevent crime by modifying the situational features of school environments. Although SCP measures are now increasingly commonplace in schools, there is inadequate research demonstrating the need for SCP measures and their impacts on school crime. In particular, there is contradictory and inconclusive evidence of their effectiveness and research has largely been limited to examining aggregate outcomes through the use non-experimental, correlational designs. This dissertation aims to address these gaps in the literature by analyzing a nationally representative, cross-sectional sample of 2,648 schools to explore whether school-based SCP measures causes changes in the incidence of seven measures of school crime and whether the effects of SCP measures differ by the type of crime. A quasi-experimental, propensity-score weighting approach is used to reduce the threat of selection bias resulting from the lack of random assignment in observational data and therefore allow for stronger causal inferences than prior studies. Findings indicate that many SCP measures were observed to have no impact regardless of the crime outcome. However, some SCP measures were reported to have deterrent effects but these effects vary by the type of crime being targeted. Furthermore, several of the measures were found to consistently increase the incidence of crime, suggestive of detection or crime-inducing effects. Explanations for these results and implications for school policy and practice are discussed

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content.  Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of  cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying  along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media

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

Offenders in emerging adulthood : School maladjustment, childhood adversities, and prediction of aggressive antisocial behaviors

Early psychosocial adversities and maladjustment, such as childhood maltreatment and school adjustment problems, have been linked to an increased risk of aggressive antisocial behaviors. Yet, clinical studies of subjects at the highest risk of persistence in such behaviors are rare, especially during the life-changing transition years of emerging adulthood. This study describes early predictors of aggressive antisocial behaviors in a large, nationally representative cohort of Swedish, male violent offenders in emerging adulthood (age range = 18-25 years; N = 270). First, data on psychosocial background characteristics and aggressive antisocial behaviors (including age at onset) are provided. Second, early predictors of aggressive antisocial behaviors are tested in bivariate and multivariate interactive models. The offenders demonstrated a diversity of early onset adversities and disruptive behaviors, in line with established risk factors for subsequent criminality and adverse outcomes in a variety of life domains. Severe school adjustment problems, especially bullying others and early onset truancy, were important and interrelated predictors of aggressive antisocial behaviors over the lifetime, whereas childhood adversities such as parental substance or alcohol abuse and repeated exposure to violence at home during childhood were interrelated predictors of aggressive antisocial behaviors, albeit with less statistical importance. The findings stress the importance of early identification of individuals in the risk zone of developing severe and persistent aggressive antisocial behaviors and of early preventive interventions directed toward families with high-risk profiles. The findings also provide initial guidelines on which psychosocial background risk factors that need to be considered first-hand in early interventions. (PsycINFO Database Recor

Social Media, Not So Social: Exploring the Ethical and Administrative Implications of Cyberbullying Research as It Pertains to Its Detection, Measurement, and Implementation of Preventative Strategies in Schools

The digital revolution in the 21st century has paved the way for the proliferation of social networking sites such as Facebook,Twitter, Instagram, Snapchat, TikTok, and others, which has helped to perpetuate civilization’s age-old power imbalances in the formof cyberbullying. This article examines how cyberbullying among adolescents is being detected, measured and mitigated, and highlights some ethical considerations for school leaders. This conceptual research paper reviewed and analyzed forty-four scholarly sources, belonging to a wide range of disciplines, from cyber ethics to computer science, which expose cyberbullying as a social justice issue. This article invites school leaders to work within the Critical Transformative Leadership for Social Justice framework when navigating the ethical challenges that may arise with cyberbullying detection, measurement and mitigation initiatives. This paper urges digitally novice adults to keep pace with digitally savvy adolescents, and for policy makers to collaborate with micro-celebrities (i.e., social media influencers) to raise awareness around cyber ethics and digital citizenship among K–12 students

Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper We define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions