The Role of Previous Discourse in Identifying Public Textual Cyberbullying

In this paper we investigate the contribution of previous discourse in identifying elements that are key to detecting public textual cyberbullying. Based on the analysis of our dataset, we first discuss the missing cyberbullying elements and the grammatical structures representative of discourse-dependent cyberbullying discourse. Then we identify four types of discourse dependent cyberbullying constructions: (1) fully inferable constructions, (2) personal marker and cyberbullying link inferable constructions, (3) dysphemistic element and cyberbullying link inferable constructions, and (4) dysphemistic element inferable constructions. Finally, we formalise a framework to resolve the missing cyberbullying elements that proposes several resolution algorithms. The resolution algorithms target the following discourse dependent message types: (1) polarity answers, (2) contradictory statements, (3) explicit ellipsis, (4) implicit affirmative answers, and (5) statements that use indefinite pronouns as placeholders for thedysphemistic element.

[1]  Dolf Trieschnigg,et al.  Improving Cyberbullying Detection with User Context , 2013, ECIR.

[2]  Pawel Dybala,et al.  Machine Learning and Affect Analysis Against Cyber-Bullying , 2010 .

[3]  Kasturi Dewi Varathan,et al.  Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network , 2016, Comput. Hum. Behav..

[4]  Kelly Reynolds,et al.  Detecting cyberbullying: query terms and techniques , 2013, WebSci.

[5]  Xue Li,et al.  An Effective Approach for Cyberbullying Detection , 2013 .

[6]  Billy Henson Bullying beyond the schoolyard: Preventing and responding to cyberbullying , 2012 .

[7]  Ying Chen,et al.  Detecting Offensive Language in Social Media to Protect Adolescent Online Safety , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[8]  D. Boyd Why Youth (Heart) Social Network Sites: The Role of Networked Publics in Teenage Social Life , 2007 .

[9]  Brian O'Neill,et al.  A lexical database for public textual cyberbullying detection , 2017 .

[10]  Shivakant Mishra,et al.  Towards understanding cyberbullying behavior in a semi-anonymous social network , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[11]  Gianluca Stringhini,et al.  Mean Birds: Detecting Aggression and Bullying on Twitter , 2017, WebSci.

[12]  D. Grigg Cyber-Aggression: Definition and Concept of Cyberbullying , 2010, Australian Journal of Guidance and Counselling.

[13]  T. Gonen,et al.  Questions , 1927, Journal of Family Planning and Reproductive Health Care.

[14]  K. Burridge,et al.  Forbidden Words: Taboo and the Censoring of Language , 2006 .

[15]  Walter Daelemans,et al.  Detection and Fine-Grained Classification of Cyberbullying Events , 2015, RANLP.

[16]  Kenji Araki,et al.  Detecting Cyberbullying Entries on Informal School Websites Based on Category Relevance Maximization , 2013, IJCNLP.

[17]  Shivakant Mishra,et al.  A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying , 2014, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing.

[18]  Brian O'Neill,et al.  Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying , 2018, Journal of Computer-Assisted Linguistic Research.

[19]  Kelly Reynolds,et al.  Using Machine Learning to Detect Cyberbullying , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[20]  Leslie Haddon,et al.  EU kids online II: final report 2011 , 2011 .

[21]  Manfred Krifka,et al.  For a Structured Meaning Account of Questions and Answers , 2001 .

[22]  D. Cross,et al.  Cyberbullying Versus Face-to-Face Bullying A Theoretical and Conceptual Review , 2009 .

[23]  Nura Kawa Text Classification , 2016 .

[24]  Ellen F. Prince,et al.  Toward a taxonomy of given-new information , 1981 .

[25]  Christopher D. Manning,et al.  Stanford typed dependencies manual , 2010 .

[26]  Colette Langos,et al.  Cyberbullying: The Challenge to Define , 2012, Cyberpsychology Behav. Soc. Netw..

[27]  Leslie Haddon,et al.  Children’s online risks and opportunities: comparative findings from EU Kids Online and Net Children Go Mobile , 2014 .

[28]  Qianjia Huang,et al.  Cyber Bullying Detection Using Social and Textual Analysis , 2014, SAM '14.

[29]  Kenji Araki,et al.  Sustainable cyberbullying detection with category-maximized relevance of harmful phrases and double-filtered automatic optimization , 2016, Int. J. Child Comput. Interact..

[30]  J. I. Sheeba,et al.  Online Social Network Bullying Detection Using Intelligence Techniques , 2015 .

[31]  A. Sourander,et al.  Psychosocial risk factors associated with cyberbullying among adolescents: a population-based study. , 2010, Archives of general psychiatry.

[32]  Christopher D. Manning,et al.  The Stanford Typed Dependencies Representation , 2008, CF+CDPE@COLING.

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

[34]  Henry Lieberman,et al.  Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying , 2012, TIIS.