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

Abstract

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.

Detecting Offensive Statements towards Foreigners in Social Media

“…The majority of the publications apply either lexicon [28,31] or machine learning approaches [14,29,30]. Lexicon approaches entirely rely on a lexicon containing offensive words typically used in hate speech.…”

“…These features are derived by experts from characteristics of hate speech messages and include, for example, the presence of offending words defined in a lexicon [14,30] and the presence of words typically referring to persons [30]. Compared to lexicon approaches, the classification performance is only slightly better [28,29,31]. Additionally, the collection of an adequate amount of training data is cumbersome due to the lack of annotated datasets [28,31].…”

Using the Control Balance Theory to Explain Social Media Deviance

“…Cyberstalking can be defined as "a group of behaviors in which an individual, group of individuals, or organization uses information and communication technology to harass another individual, group of individuals, or organization" [70, p. 393]. OSMD behaviors such as cyberbullying and cyberstalking have been universally termed as deviant behaviors since they cause harm to others and violate basic human rights of selfpreservation [16].…”

“…Given these serious issues with OSMD, researchers have recognized the need for additional research focused on the causes and prevention of OSMD [16]. Although some notable efforts have been initiated, several gaps remain, especially in designing programs to prevent such behaviors [4].…”

Sentiment Informed Cyberbullying Detection in Social Media

“…Crime Prevention Council, more than 40% of the teens in the US have been bullied on various social media platforms Dinakar et al (2012). The victims of the cyberbullying often suffer from depression, loneliness, anxiety, and low self-esteem Xu et al (2012a).…”