Online Social Network Bullying Detection Using Intelligence Techniques

AbstractSocial networking sites (SNS) is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. However, Social Networking Sites is providing opportunities for cyberbullying activities. Cyberbullying is harassing or insulting a person by sending messages of hurting or threatening nature using electronic communication. Cyberbullying poses significant threat to physical and mental health of the victims.Detection of cyberbullying and the provision of subsequent preventive measures are the main courses of action to combat cyberbullying. The proposed method is an effective method to detect cyberbullying activities on social media. The detection method can identify the presence of cyberbullying terms and classify cyberbullying activities in social network such as Flaming, Harassment, Racism and Terrorism, using Fuzzy logic and Genetic algorithm. The effectiveness of the system is increased using Fuzzy rule set to retrieve relevant data for classification from the input. In the proposed method Genetic algorithm is also used, for optimizing the parameters and to obtain precise output

Designing a Patient-Centered Clinical Workflow to Assess Cyberbully Experiences of Youths in the U.S. Healthcare System

Cyberbullying or online harassment is often defined as when someone repeatedly and intentionally harasses, mistreats, or makes fun of others aiming to scare, anger or shame them using electronic devices [296]. Youths experiencing cyberbullying report higher levels of anxiety and depression, mental distress, suicide thoughts, and substance abuse than their non-bullied peers [360, 605, 261, 354]. Even though bullying is associated with significant health problems, to date, very little youth anti-bullying efforts are initiated and directed in clinical settings. There is presently no standardized procedure or workflow across health systems for systematically assessing cyberbullying or other equally dangerous online activities among vulnerable groups like children or adolescents [599]. Therefore, I developed a series of research projects to link digital indicators of cyberbullying or online harassment to clinical practices by advocating design considerations for a patient-centered clinical assessment and workflow that addresses patients’ needs and expectations to ensure quality care. Through this dissertation, I aim to answer these high-level research questions:RQ1. How does the presence of severe online harassment on online platforms contribute to negative experiences and risky behaviors within vulnerable populations? RQ2. How efficient is the current mechanism of screening these risky online negative experiences and behaviors, specifically related to cyberbully, within at-risk populations like adolescent in clinical settings? RQ3. How might evidence of activities and negative harassing experiences on online platforms best be integrated into electronic health records during clinical treatment? I first explore how harassment is presented within different social media platforms from diverse contexts and cultural norms (study 1,2, and 3); next, by analyzing actual patient data, I address current limitations in the screening process in clinical settings that fail to efficiently address core aspect of cyberbullying and their consequences within adolescent patients (study 4 and 5); finally, connecting all my findings, I recommend specific design guidelines for a refined screening tool and structured processes for implementation and integration of the screened data into patients’ electronic health records (EHRs) for better patient assessment and treatment outcomes around cyberbully within adolescent patients (study 6)

Self-disclosure model for classifying & predicting text-based online disclosure

Les médias sociaux et les sites de réseaux sociaux sont devenus des babillards numériques pour les internautes à cause de leur évolution accélérée. Comme ces sites encouragent les consommateurs à exposer des informations personnelles via des profils et des publications, l'utilisation accrue des médias sociaux a généré des problèmes d’invasion de la vie privée. Des chercheurs ont fait de nombreux efforts pour détecter l'auto-divulgation en utilisant des techniques d'extraction d'informations. Des recherches récentes sur l'apprentissage automatique et les méthodes de traitement du langage naturel montrent que la compréhension du sens contextuel des mots peut entraîner une meilleure précision que les méthodes d'extraction de données traditionnelles. Comme mentionné précédemment, les utilisateurs ignorent souvent la quantité d'informations personnelles publiées dans les forums en ligne. Il est donc nécessaire de détecter les diverses divulgations en langage naturel et de leur donner le choix de tester la possibilité de divulgation avant de publier. Pour ce faire, ce travail propose le « SD_ELECTRA », un modèle de langage spécifique au contexte. Ce type de modèle détecte les divulgations d'intérêts, de données personnelles, d'éducation et de travail, de relations, de personnalité, de résidence, de voyage et d'accueil dans les données des médias sociaux. L'objectif est de créer un modèle linguistique spécifique au contexte sur une plate-forme de médias sociaux qui fonctionne mieux que les modèles linguistiques généraux. De plus, les récents progrès des modèles de transformateurs ont ouvert la voie à la formation de modèles de langage à partir de zéro et à des scores plus élevés. Les résultats expérimentaux montrent que SD_ELECTRA a surpassé le modèle de base dans toutes les métriques considérées pour la méthode de classification de texte standard. En outre, les résultats montrent également que l'entraînement d'un modèle de langage avec un corpus spécifique au contexte de préentraînement plus petit sur un seul GPU peut améliorer les performances. Une application Web illustrative est conçue pour permettre aux utilisateurs de tester les possibilités de divulgation dans leurs publications sur les réseaux sociaux. En conséquence, en utilisant l'efficacité du modèle suggéré, les utilisateurs pourraient obtenir un apprentissage en temps réel sur l'auto-divulgation.Social media and social networking sites have evolved into digital billboards for internet users due to their rapid expansion. As these sites encourage consumers to expose personal information via profiles and postings, increased use of social media has generated privacy concerns. There have been notable efforts from researchers to detect self-disclosure using Information extraction (IE) techniques. Recent research on machine learning and natural language processing methods shows that understanding the contextual meaning of the words can result in better accuracy than traditional data extraction methods. Driven by the facts mentioned earlier, users are often ignorant of the quantity of personal information published in online forums, there is a need to detect various disclosures in natural language and give them a choice to test the possibility of disclosure before posting. For this purpose, this work proposes "SD_ELECTRA," a context-specific language model to detect Interest, Personal, Education and Work, Relationship, Personality, Residence, Travel plan, and Hospitality disclosures in social media data. The goal is to create a context-specific language model on a social media platform that performs better than the general language models. Moreover, recent advancements in transformer models paved the way to train language models from scratch and achieve higher scores. Experimental results show that SD_ELECTRA has outperformed the base model in all considered metrics for the standard text classification method. In addition, the results also show that training a language model with a smaller pre-training context-specific corpus on a single GPU can improve its performance. An illustrative web application designed allows users to test the disclosure possibilities in their social media posts. As a result, by utilizing the efficiency of the suggested model, users would be able to get real-time learning on self-disclosure

Detection and Prevention of Cyberbullying on Social Media

The Internet and social media have undoubtedly improved our abilities to keep in touch with friends and loved ones. Additionally, it has opened up new avenues for journalism, activism, commerce and entertainment. The unbridled ubiquity of social media is, however, not without negative consequences and one such effect is the increased prevalence of cyberbullying and online abuse. While cyberbullying was previously restricted to electronic mail, online forums and text messages, social media has propelled it across the breadth of the Internet, establishing it as one of the main dangers associated with online interactions. Recent advances in deep learning algorithms have progressed the state of the art in natural language processing considerably, and it is now possible to develop Machine Learning (ML) models with an in-depth understanding of written language and utilise them to detect cyberbullying and online abuse. Despite these advances, there is a conspicuous lack of real-world applications for cyberbullying detection and prevention. Scalability; responsiveness; obsolescence; and acceptability are challenges that researchers must overcome to develop robust cyberbullying detection and prevention systems. This research addressed these challenges by developing a novel mobile-based application system for the detection and prevention of cyberbullying and online abuse. The application mitigates obsolescence by using different ML models in a “plug and play” manner, thus providing a mean to incorporate future classifiers. It uses ground truth provided by the enduser to create a personalised ML model for each user. A new large-scale cyberbullying dataset of over 62K tweets annotated using a taxonomy of different cyberbullying types was created to facilitate the training of the ML models. Additionally, the design incorporated facilities to initiate appropriate actions on behalf of the user when cyberbullying events are detected. To improve the app’s acceptability to the target audience, user-centred design methods were used to discover stakeholders’ requirements and collaboratively design the mobile app with young people. Overall, the research showed that (a) the cyberbullying dataset sufficiently captures different forms of online abuse to allow the detection of cyberbullying and online abuse; (b) the developed cyberbullying prevention application is highly scalable and responsive and can cope with the demands of modern social media platforms (b) the use of user-centred and participatory design approaches improved the app’s acceptability amongst the target audience

Sticks and Stones May Break My Bones but Words Will Never Hurt Me...Until I See Them: A Qualitative Content Analysis of Trolls in Relation to the Gricean Maxims and (IM)Polite Virtual Speech Acts

The troll is one of the most obtrusive and disruptive bad actors on the internet. Unlike other bad actors, the troll interacts on a more personal and intimate level with other internet users. Social media platforms, online communities, comment boards, and chatroom forums provide them with this opportunity. What distinguishes these social provocateurs from other bad actors are their virtual speech acts and online behaviors. These acts aim to incite anger, shame, or frustration in others through the weaponization of words, phrases, and other rhetoric. Online trolls come in all forms and use various speech tactics to insult and demean their target audiences. The goal of this research is to investigate trolls\u27 virtual speech acts and the impact of troll-like behaviors on online communities. Using Gricean maxims and politeness theory, this study seeks to identify common vernacular, word usage, and other language behaviors that trolls use to divert the conversation, insult others, and possibly affect fellow internet users’ mental health and well-being

Design, Deployment, Identity, & Conformity: An Analysis of Children's Online Social Networks

Preadolescents (children aged 7 to 12 years) are participating in online social networks whether we, as a society, like it or not. The Children’s Online Privacy Protection Act, enacted by the United States Congress in 1998, made illegal the collection of online data about children under the age of 13 without express parental consent. As such, most mainstream social networks, such as Twitter, Facebook, and Instagram, limit their registration by requiring new users to agree that they are at least 13 years of age, an assertion which is often falsified. Researchers, bound by the same legal requirements regarding online data collection, have resorted to surveys and interviews to understand how and why children interact on social networks. While valuable, these prior works explain only what children say they do online, and not what they actually do on a daily basis. In this work, we describe the design, development, deployment, and analysis of our own online social network for children, KidGab. This work explores common social networking affordances for adults and their suitability for child audiences. It analyzes the participatory behaviors of our users (Girl Scouts from around central Texas) and describes how they shaped KidGab’s continuing growth. This work discusses our quantitative analysis of users’ tendencies and proclivities toward identity exploration leverages graph algorithms and link analysis techniques to understand the sociality of conformity on the network. Finally, this work describes the lessons we learned about children’s social networks and social networking throughout KidGab’s 450 days of active deployment

Towards a mobile application to aid law enforcement in diagnosing and preventing mobile bully-victim behaviour in Eastern Free State High Schools of South Africa

Mobile bully-victim behaviour is one cyber aggression that is escalating worldwide. Bully-victims are people who bully others but are also victimised by peers. The behaviour of bully-victims therefore swings between that of pure bullies and pure victims, making it difficult to identify and prevent. Prevention measures require the involvement of a number of stakeholders, including communities. However, there has been a lack of whole-community participation in the fight against cyberbullying and the roles of stakeholders are often unclear. We expect the law enforcement in particular, the police, to play a key role in curbing all forms of bullying. This is a challenging task in South Africa as these law enforcement agents often lack the skills and appropriate legislation to address particularly cyber-related bullying. Literature shows that law enforcement agents need to advance their technological skills and also be equipped with digital interventions if they are to diagnose and prevent mobile bully-victim behaviour effectively. This is particularly important in South Africa, where the rate of crime remains one of the highest in the world. The aim of this study was to develop a mobile application that can aid law enforcement in diagnosing and preventing mobile bully-victim behaviour in high schools. As part of requirements to the application development, it identified the impediments to the law enforcement effectiveness in combating mobile bully-victim behaviour. Extensive literature review on the factors influencing mobile bullying and mobile bully-victim behaviour was conducted and an integrative framework for understanding this behaviour and its prevention was developed. In so doing, the dominant behavioural theories were consulted, including the social-ecological theory, social learning theory, social information processing theories, and the theory of planned behaviour, as well as the general strain theory, and the role theory. The conceptual framework developed in this study extended and tailored the “Cyberbullying Continuum of Harm”, enabling inclusive and moderated diagnosis of bullying categories and severity assessment. That is, instead of focusing on mobile bully-victims only, bullies, victims, and those uninvolved were also identified. Also the physical moderation of the identification process by the police helped to minimise dishonest reporting. This framework informed the design, development and evaluation of a mobile application for the law enforcement agents. The Design Science Research (DSR) methodology within pragmatic paradigm and literature guided the development of the mobile application named mobile bullyvictims response system (M-BRS) and its evaluation for utility. The M-BRS features included functions to enable anonymous reporting and confidential assessments of mobile bully-victims effects in school classrooms. Findings from this study confirmed the utility of the M-BRS to identify learners' involvement in mobile bully-victims behaviour through peer nomination and self-nomination. This study also showed that use of the M-BRS has enabled empowerment of marginalised learners, and mitigation of learners' fear to report, providing them with control over mobile bully-victim reporting. In addition, learners using the M-BRS were inclined to report perpetrators through a safe (anonymous and confidential) reporting platform. With the M-BRS, it was much easier to identify categories of bullies, i.e. mobile bully-victims, bullies, victims, and uninvolved. The practical contributions of this study were skills enhancements in reducing the mobile bully-victims behaviour. These included improvement of the police's technical skills to safely identify mobile bully-victims and their characterisation as propagators and retaliators that enabled targeted interventions. This was particularly helpful in response to courts' reluctance to prosecute teenagers for cyberbullying and the South African lack of legislation thereon so that the police are enabled to restoratively address this behaviour in schools. Also, the identification information was helpful to strengthen evidence for reported cases, which was remarkable because sometimes perpetrators cannot be found due to their concealed online identities. Furthermore, this study made possible the surveillance of mobile bully-victims through the M-BRS, which provided the police some control to reducing the mobile bully-victim behaviour. This study provided a practical way for implementing targeted prevention and interventions programmes using relevant resources towards a most efficient solution for mobile bully-victims problem. Since there are not many mobile-based interventions for mobile bully-victim behaviour, this study provided a way in which artefacts' development could be informed by theory, as a new, innovative and practical contribution in research. In so doing, this study contributed to technology applications' ability to modify desired behaviour

Cyber Infrastructure Protection: Vol. III

Despite leaps in technological advancements made in computing system hardware and software areas, we still hear about massive cyberattacks that result in enormous data losses. Cyberattacks in 2015 included: sophisticated attacks that targeted Ashley Madison, the U.S. Office of Personnel Management (OPM), the White House, and Anthem; and in 2014, cyberattacks were directed at Sony Pictures Entertainment, Home Depot, J.P. Morgan Chase, a German steel factory, a South Korean nuclear plant, eBay, and others. These attacks and many others highlight the continued vulnerability of various cyber infrastructures and the critical need for strong cyber infrastructure protection (CIP). This book addresses critical issues in cybersecurity. Topics discussed include: a cooperative international deterrence capability as an essential tool in cybersecurity; an estimation of the costs of cybercrime; the impact of prosecuting spammers on fraud and malware contained in email spam; cybersecurity and privacy in smart cities; smart cities demand smart security; and, a smart grid vulnerability assessment using national testbed networks.https://press.armywarcollege.edu/monographs/1412/thumbnail.jp

Aspects of internet security: identity management and online child protection

This thesis examines four main subjects; consumer federated Internet Identity Management (IdM), text analysis to detect grooming in Internet chat, a system for using steganographed emoticons as ‘digital fingerprints’ in instant messaging and a systems analysis of online child protection. The Internet was never designed to support an identity framework. The current username / password model does not scale well and with an ever increasing number of sites and services users are suffering from password fatigue and using insecure practises such as using the same password across websites. In addition users are supplying personal information to vast number of sites and services with little, if any control over how that information is used. A new identity metasystem promises to bring federated identity, which has found success in the enterprise to the consumer, placing the user in control and limiting the disclosure of personal information. This thesis argues though technical feasible no business model exists to support consumer IdM and without a major change in Internet culture such as a breakdown in trust and security a new identity metasystem will not be realised. Is it possible to detect grooming or potential grooming from a statistical examination of Internet chat messages? Using techniques from speaker verification can grooming relationships be detected? Can this approach improve on the leading text analysis technique – Bayesian trigram analysis? Using a novel feature extraction technique and Gaussian Mixture Models (GMM) to detect potential grooming proved to be unreliable. Even with the benefit of extensive tuning the author doubts the technique would match or improve upon Bayesian analysis. Around 80% of child grooming is blatant with the groomer disguising neither their age nor sexual intent. Experiments conducted with Bayesian trigram analysis suggest this could be reliably detected, detecting the subtle, devious remaining 20% is considerably harder and reliable detection is questionable especially in systems using teenagers (the most at risk group). Observations of the MSN Messenger service and protocol lead the author to discover a method by which to leave digitally verifiable files on the computer of anyone who chats with a child by exploiting the custom emoticon feature. By employing techniques from steganography these custom emoticons can be made to appear innocuous. Finding and removing custom emoticons is a non-trivial matter and they cannot be easily spoofed. Identification is performed by examining the emoticon (file) hashes. If an emoticon is recovered e.g. in the course of an investigation it can be hashed and the hashed compared against a database of registered users and used to support non-repudiation and confirm if an individual has indeed been chatting with a child. Online child protection has been described as a classic systems problem. It covers a broad range of complex, and sometimes difficult to research issues including technology, sociology, psychology and law, and affects directly or indirectly the majority of the UK population. Yet despite this the problem and the challenges are poorly understood, thanks in no small part to mawkish attitudes and alarmist media coverage. Here the problem is examined holistically; how children use technology, what the risks are, and how they can best be protected – based not on idealism, but on the known behaviours of children. The overall protection message is often confused and unrealistic, leaving parents and children ill prepared to protect themselves. Technology does have a place in protecting children, but this is secondary to a strong and understanding parent/child relationship and education, both of the child and parent

An exploratory study on new technology and associated psychosocial risks in adolescents : can digital media literacy programmes make a difference

This study centres on the psychological effects new digital media, like the internet and cellphones, have on adolescents. Although the internet has enormous benefits, it also poses a host of risks that can make adolescents vulnerable to victimisation and/or developing associated psychosocial problems. Characterisations of adolescents’ social relationships in the internet medium, as well as the investigation of the continuity between digital media literacy and online social behaviours, carry high relevance for developmental psychology. It is during the adolescent period that peer interactions arguably hold the greatest importance for individuals’ social and behavioural functioning. Using a logic model for evaluation, the researcher conducted an exploratory research study on digital media use among adolescent learners aged 13 to 15 years to determine whether schools could guide them to think critically for themselves about the entire realm of these new media. The data were gathered from school principals, teachers, parents and learners from three secondary schools in Gauteng Province, which were purposely selected to represent different socio-economic circumstances. A total of 230 people (n=230) participated in the research. Mixed research methods were employed in this study. The quantitative research methods supported the qualitative research methods. The literature review suggested that current media literacy education, which forms part of the Life Orientation curriculum, does not enable learners to think critically or make informed choices about their behaviour in the digital world – because it incorporates neither ethics nor responsibility. One of the main aims of the study therefore was to investigate the importance of expanding existing media literacy education, namely by incorporating two additional learning categories in the curriculum: Digital Safety and Security, and Digital Citizenship. These additional learning categories were introduced in the form of lessons by the teachers participating in the study. A think aloud strategy was used whereby learners verbalise what they were doing and learning while engaging in the digital media literacy lesson activities. The learners’ verbalisations were used to ascertain what learning was occurring in the classroom. The experimental group demonstrated an increase in critical thinking from pre- to post-evaluation. This research therefore proposes that the signature element of intervention strategies for inappropriate online behaviour be to create a “culture of critical thinking”. This implies greatly reducing the risks cyberspace pose, and at the same time enhancing adolescents’ abilities to use it in ways that create and deepen healthy relationships – in the digital as well as the real world.PsychologyD. Litt. et Phil. (Psychology