Toward Multimodal Cyberbullying Detection

Abstract

As human beings utilize computing technologies to mediate multiple aspects of their lives, cyberbullying has grown as an important societal challenge. Cyberbullying may lead to deep psychiatric and emotional disorders for those affected. Hence, there is an urgent need to devise automated methods for cyberbullying detection and prevention. While recent cyberbullying detection efforts have defined sophisticated text processing methods for cyberbullying detection, there are as yet few efforts that leverage visual data processing to automatically detect cyberbullying. Based on early analysis of a public, labeled cyberbullying dataset, we report that visual features complement textual features in cyberbullying detection and can help improve predictive results.

Social Networking – Boon or Bane? Student’s Perception of the Role Social Networking Plays in Helping (or Hindering) Learning

“…According to Claywell (2016), cyberbullying is often levied more harshly against young females than males and, unlike traditional bullying, it is not limited to physical interaction. While there are existing methodologies and software tools that can help parents and school administrators identify precursors and instances of cyberbullying, there still is much to do to create automated ways to detect cyberbullying and to prevent it (Singh, Ghosh, & Jose, 2017;Wohn, Fiesler, Hemphill, De Choudhury, & Matias, 2017). Aside from user awareness support programs, network-based built-in systems to alert parents or school administrators will definitely help minimize the damage that these online bullies inflict.…”

Attitude of Adolescents Towards Use of Social Media

“…60% of them strongly agree or agree that adolescents have tendency to abuse social media for their popularity. Singh, et al, (2017) noted that the adolescent's feeling of social acceptance and attention, and the need to impress their online peer audience can result in risky or unhealthy behavior in the offline world. One of the most common methods of deception on social media is the use of fake profiles, where malicious users create profiles to impersonate fictitious or real persons, such as celebrities or other people in the public interest More than 50 % of the adolescents agreed that cybercrime are very common in social media and many of the adolescents are victim of cyber bullying.…”

Cyberbullies in Twitter: A focused review

“…There are some works that do not make use of text as the primary source of information for abuse detection. Singh et al [13] report that there are some visual features that complement textual features in cyberbullying detection and can help improve predictive results. They used the Instagram as their data source platform [14] and through the Microsoft's Project Oxford (a computer vision API) extracted visual features from the Instagram images].…”