Cyberbullying detection: a step toward a safer internet yard

As a result of the invention of social networks friendships, relationships and social communications have all gone to a new level with new definitions. One may have hundreds of friends without even seeing their faces. Meanwhile, alongside this transition there is increasing evidence that online social applications have been used by children and adolescents for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the users involved in cyberbullying. We propose that incorporation of the users' information, their characteristics, and post-harassing behaviour, for instance, posting a new status in another social network as a reaction to their bullying experience, will improve the accuracy of cyberbullying detection. Cross-system analyses of the users' behaviour - monitoring their reactions in different online environments - can facilitate this process and provide information that could lead to more accurate detection of cyberbullying.

[1]  D. Espelage,et al.  Bullying and Victimization : What Have We Learned and Where Do We Go from Here ? [ Mini-Series ] , 2017 .

[2]  Qi Gao,et al.  Analyzing Cross-System User Modeling on the Social Web , 2011, ICWE.

[3]  Bart Goethals,et al.  Automatic Vandalism Detection in Wikipedia : Towards a Machine Learning Approach , 2008 .

[4]  Robin M. Kowalski,et al.  Cyber Bullying: Bullying in the Digital Age , 2007 .

[5]  Federica Cena,et al.  User identification for cross-system personalisation , 2009, Inf. Sci..

[6]  Nicola Henze,et al.  Linkage, aggregation, alignment and enrichment of public user profiles with Mypes , 2010, I-SEMANTICS '10.

[7]  June F. Chisholm,et al.  Cyberspace Violence against Girls and Adolescent Females , 2006, Annals of the New York Academy of Sciences.

[8]  John Riedl,et al.  Tagommenders: connecting users to items through tags , 2009, WWW '09.

[9]  Henry Lieberman,et al.  Modeling the Detection of Textual Cyberbullying , 2011, The Social Mobile Web.

[10]  Sarah Steiner Gender, Genre, and Writing Style in Formal Written Texts , 2014 .

[11]  Geert-Jan Houben,et al.  Cross-system user modeling and personalization on the Social Web , 2013, User Modeling and User-Adapted Interaction.

[12]  Anto Satriyo Nugroho,et al.  Text Classification Techniques Used to Faciliate Cyber Terrorism Investigation , 2010, 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[13]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[14]  Pang-Ning Tan,et al.  1 INFORMATION ASSURANCE : DETECTION OF WEB SPAM ATTACKS IN SOCIAL MEDIA , 2010 .

[15]  Brian D. Davison,et al.  Detection of Harassment on Web 2.0 , 2009 .

[16]  Marilyn A. Campbell,et al.  Cyber Bullying: An Old Problem in a New Guise? , 2005, Australian Journal of Guidance and Counselling.

[17]  Peter K. Smith,et al.  Cyberbullying: its nature and impact in secondary school pupils. , 2008, Journal of child psychology and psychiatry, and allied disciplines.

[18]  Anat Rachel Shimoni,et al.  Gender, genre, and writing style in formal written texts , 2003 .

[19]  Lynne Edwards,et al.  ChatCoder: Toward the Tracking and Categorization of Internet Predators , 2009 .

[20]  R. Ordelman,et al.  Improved cyberbullying detection using gender information , 2012 .

[21]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.