Detecting cyberbullying and cyberaggression in social media

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future

Features for Detecting Aggression in Social Media: An Exploratory Study

Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the “help" of the widespread of social media networks, bullying once limited to particular places or times of the day, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour intending to cause harm to another person, involving rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the gravity of the consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), there is an imperious need for research aiming at understanding how cyberbullying occurs, in order to prevent it from escalating. Given the massive information overload on the Web, it is crucial to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. Considering the challenges posed by the characteristics of social media content and the cyberaggression task, this paper focuses on the detection of aggressive content in the context of multiple social media sites by exploring diverse types of features. Experimental evaluation conducted on two real-world social media dataset showed the difficulty of the task, confirming the limitations of traditionally used features.Sociedad Argentina de Informática e Investigación Operativ

Mechanisms of Moral Disengagement in the Transition from Cybergossip to Cyberaggression: A Longitudinal Study

The internet is an area where young people establish relationships and develop socially, emotionally and morally, but it also gives rise to certain forms of online behaviour, such as cybergossip, which are associated with cyberaggression and other risky behaviour. The aims of this study were to verify whether a longitudinal association exists between cybergossip and cyberaggression, and to discover which mechanisms of moral disengagement may mediate this relationship. The final sample consisted of 1392 students (50% girls; Mage = 13.47; SD = 0.77), who were surveyed in a three-wave longitudinal study at six-month intervals. The results obtained confirmed a direct, positive relationship between cybergossip, subsequent cyberaggression and the mediation exerted by cognitive restructuring in this transition. We discuss the importance of recognizing and detecting the fine distinction between online gossip and cyberaggression with the intention of doing harm, and focus on the justifications used by young people to normalize online bullying. To sum up, there is a clear need to encourage ethical, responsible behaviour in online interactions in order to achieve well-balanced, more sustainable relationships in classrooms

Generalization of cyberbullying traces

De nos jours, la cyberintimidation est un problème courant dans les communautés en ligne. Filtrer automatiquement ces messages de cyberintimidation des conversations en ligne c’est avéré être un défi qui a mené à la création de plusieurs ensembles de données, dont plusieurs disponibles comme ressources pour l’entraînement de classificateurs. Toutefois, sans consensus sur la définition de la cyberintimidation, chacun des ensembles de données se retrouve à documenter différentes formes de comportements. Cela rend difficile la comparaison des performances obtenues par de classificateurs entraînés sur de différents ensembles de données, ou même l’application d’un de ces classificateurs à un autre ensemble de données. Dans ce mémoire, on utilise une variété de ces ensembles de données afin d’explorer les différentes définitions, ainsi que l’impact que cela occasionne sur le langage utilisé. Par la suite, on explore la portabilité d’un classificateur entraîné sur un ensemble de données vers un autre ensemble, nous donnant ainsi une meilleure compréhension de la généralisation des classificateurs. Finalement, on étudie plusieurs architectures d’ensemble de modèles, qui par la combinaison de ces différents classificateurs, nous permet de mieux comprendre les interactions des différentes définitions. Nos résultats montrent qu’il est possible d’obtenir une meilleure généralisation en combinant tous les ensembles de données en un seul ensemble de données plutôt que d’utiliser un ensemble de modèles composé de plusieurs classificateurs, chacun entraîné individuellement sur un ensemble de données différent.Cyberbullying is a common problem in today’s ubiquitous online communities. Automatically filtering it out of online conversations has proven a challenge, and the efforts have led to the creation of many different datasets, which are distributed as resources to train classifiers. However, without a consensus for the definition of cyberbullying, each of these datasets ends up documenting a different form of the behavior. This makes it difficult to compare the results of classifiers trained on different datasets, or to apply one such classifier on a different dataset. In this thesis, we will use a variety of these datasets to explore the differences in their definitions of cyberbullying and the impact it has on the language used in the messages. We will then explore the portability of a classifier trained on one dataset to another in order to gain insight on the generalization power of classifiers trained from each of them. Finally, we will study various architectures of ensemble models combining these classifiers in order to understand how they interact with each other. Our results show that by combining all datasets together into a single bigger one, we can achieve a better generalization than by using an ensemble model of individual classifiers trained on each dataset

Exploring Construct Validity and Measurement Invariance of the Cyberbullying Experiences Survey

Given recent calls for advancing valid instrumentation in the field of cyberaggression, the present study evaluated construct validity and measurement invariance for the Cyberbullying Experiences Survey (CES) in a high school and college student sample. A series of confirmatory factor analyses (CFA), reliability analyses, and a nomological net evaluation were conducted to address these aims. The data did not provide support for the hypothesized four-factor model for cyberaggression or cybervictimization (i.e., unwanted contact, malice, deception, and public humiliation). Upon implementing suggested and theoretically supported modification indices, support for a four-factor solution for both cyberaggression and cybervictimization was provided. To subsequently evaluate measurement invariance, single-group CFAs were constructed to test invariance of the four-factor structure across college and high school students. Results provided support for the four-factor model solution of cyberaggression and cybervictimization in the college sample but not in the high school sample. Two cyberaggression subscales (i.e., unwanted contact and deception) correlated at r = .99, indicating the potential for multicollinearity, and incremental fit indices for the cybervictimization model solution did not meet recommended cut-off values in the high school sample. Revised model results based on statistical and theoretical considerations evaluated a restructured three-factor solution for cyberaggression (i.e., “sexual,” “direct,” and “coercion”) and cybervictimization (i.e., “sexual,” “direct,” and “defamation”). Fit indices provided initial support for the revised model solution for both CES cyberaggression items (College: MLM c2 (163) = 273.01, RMSEA = .04, CFI = .92, SRMR = .06; High School: MLM c2 (165) = 196.29, RMSEA = .03, CFI = .96, SRMR = .08) and cybervictimization items (College: MLM c2 (163) = 367.81, RMSEA = .05, CFI = .93, SRMR = .06; High School: MLM c2 (160) = 256.32, RMSEA = .06, CFI = .92, SRMR = .07). Utilizing the revised factor solution for the remaining analyses, the CES displayed evidence for internal consistency reliability across college (cyberaggression items: α = .83; cybervictimization items: α = .89) and high school (cyberaggression items: α = .88; cybervictimization items: α = .90), although internal consistencies for the CES cyberaggression subscales ranged from poor to good (α = .54 - .88) and acceptable to excellent (α = .76 - .92) for the CES cybervictimization subscales across both college and high school samples. Evidence for convergent validity with theoretically similar constructs was mixed. Specific areas of model misspecification as well as directions for future cyberaggression measurement research and policy are discussed

Scalable and Timely Detection of Cyberbullying in Online Social Networks

The exponential growth of popularity of online social networks in the last decade has unfortunately paved the way for the threat of cyberbullying to rise to an unprecedented level. So a research that provides insights into the analysis of cyberbullying incidents and building a system that is highly scalable and responsive is of unparalleled need. This dissertation gathers insights into cyberbullying incidents in video and image-based social networks (Vine and Instagram respectively) and then presents a system solution that makes use of the gained insights to improve efficiency and efficacy of cyberbullying detection. First, it presents detailed analyses of cyberbullying incidents in the Vine social network by collecting data and labeling them by CrowdFlower. Second, it performs a thorough investigation of the differentiating factors of cyberbullying in online social networks. Third, it implements a highly scalable and responsive system solution for cyberbullying detection along with a comprehensive evaluation of its performances in terms of timeliness and scalability against a highly popular online social network. Fourth, it outlines design, implementation and preliminary user experience analysis of an android application, BullyAlert, that was developed to enable guardians to get adaptive notifications for cyberbullying based on their individual subjective tolerance levels. Finally, it shows that using textual and video feature greatly improves cyberbullying detection classifier's performances