Content based approach to find the credibility of user in social networks: an application of cyberbullying

AbstractCyberbullying is derogatory act carried out intentionally by sending or posting harmful material on social networks to cheat or tarnish anybody’s image in real world. Today it has become a significant problem among teenagers and kids as they spend much time on social networking. Two types of cyberbullying have been observed in the messages posted on social network: direct cyberbullying and indirect cyberbullying. Direct cyberbullying is to send disrespectful/abusing material in the form of text, images, videos and audios to harass/torture individual directly. Indirect cyberbullying is to attack or torture individuals indirectly by doing activities like sending objectionable contents such as false rumors, lies etc. concerning them, tagging their embarrassing images, refuse to socialize with the victim. These type of activities can be viewed by large number of audience on social media. Ground breaking research is being carried out only on the identification of cyberbullying and not on its categories such as direct and indirect cyberbullying. As indirect cyberbullying is much harmful than direct cyberbullying due to the messages posted online are visible to large number of users, which may adversely impact the victim’s reputation/position. So, it becomes necessary to find the solution for this problem. In this paper, we first categorize the messages into direct and indirect bullying messages and then proceed to find the solution for controlling the bullying through checking the credibility of user .

[1]  Dolf Trieschnigg,et al.  Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies , 2014, Canadian Conference on AI.

[2]  Dolf Trieschnigg,et al.  Towards User Modelling in the Combat against Cyberbullying , 2012, NLDB.

[3]  Stephanie Pieschl,et al.  Relevant dimensions of cyberbullying — Results from two experimental studies , 2013 .

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

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

[6]  Yuval Elovici,et al.  Friend or foe? Fake profile identification in online social networks , 2013, Social Network Analysis and Mining.

[7]  Russell A. Sabella Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress , 2007 .

[8]  Marc Lemercier,et al.  A dynamic approach to detecting suspicious profiles on social platforms , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

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

[10]  Val Besag,et al.  Cyber Bullying: Bullying in the Digital Age , 2010 .

[11]  Simon Fong,et al.  Not Every Friend on a Social Network Can be Trusted: An Online Trust Indexing Algorithm , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

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

[13]  Atsushi Tagami,et al.  A Study of Contact Network Generation for Cyber-bullying Detection , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[14]  Mauro Conti,et al.  FakeBook: Detecting Fake Profiles in On-Line Social Networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[15]  Dolf Trieschnigg,et al.  Expert knowledge for automatic detection of bullies in social networks , 2013 .

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

[17]  Hein S. Venter,et al.  Mobile cyber-bullying: A proposal for a pre-emptive approach to risk mitigation by employing digital forensic readiness , 2011, 2011 Information Security for South Africa.

[18]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[19]  Chaoyi Pang,et al.  Semi-supervised Learning for Cyberbullying Detection in Social Networks , 2014, ADC.

[20]  Jennifer D. Shapka,et al.  Are Cyberbullies really bullies? An investigation of reactive and proactive online aggression , 2012, Comput. Hum. Behav..

[21]  Jaideep Srivastava,et al.  Predicting trusts among users of online communities: an epinions case study , 2008, EC '08.

[22]  Chaoyi Pang,et al.  Sentiment Analysis for Effective Detection of Cyber Bullying , 2012, APWeb.

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

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

[25]  Igor Santos,et al.  Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying , 2015, Log. J. IGPL.