Year 2019, Volume 3, Issue 4, Pages 168 - 178 2019-10-01

AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS

Çiğdem ACI [1] , Eren Çürük [2] , Esra Saraç Eşsiz [3]

16 26

Cyberbullying has become a major problem along with the increase of communication technologies and social media become part of daily life. Cyberbullying is the use of communication tools to harass or harm a person or group. Especially for the adolescent age group, cyberbullying causes damage that is thought to be suicidal and poses a great risk. In this study, a model is developed to identify the cyberbullying actions that took place in social networks. The model investigates the effects of some text mining methods such as pre-processing, feature extraction, feature selection and classification on automatic detection of cyberbullying using datasets obtained from Formspring.me, Myspace and YouTube social network platforms. Different classifiers (i.e. multilayer perceptron (MLP), stochastic gradient descent (SGD), logistic regression and radial basis function) have been developed and the effects of feature selection algorithms (i.e. Chi2, support vector machine-recursive feature elimination (SVM-RFE), minimum redundancy maximum relevance and ReliefF) for cyberbullying detection have also been investigated. The experimental results of the study proved that SGD and MLP classifiers with 500 selected features using SVM-RFE algorithm showed the best results (F_measure value is more than 0.930) by means of classification time and accuracy.

cyberbullying, automatic detection, social networks, feature selection, classification
  • Al-garadi, M.A., Varathan, K.D. and Ravana, S.D. (2016). “Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network:” Computers in Human Behavior, Vol. 63, pp. 433–443.
  • Andreou, E. (2004). “Bully/victim problems and their association with Machiavellianism and self-efficacy in Greek primary school children:” British Journal of Educational Psychology, Vol. 74, No. 2, pp. 297–309.
  • Bing Liu (2011). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd Ed., Springer Publishing Company.
  • Chakrabarti, S. (2003). Mining the Web : discovering knowledge from hypertext data, Morgan Kaufmann.
  • Dadvar, M. and Jong, F.M.G. de (2012). Improved Cyberbullying Detection Through Personal Profiles. International Conference on Cyberbullying, Paris, France.
  • Dadvar, M., Jong, F.M.G. de, Ordelman, R.J.F. and Trieschnigg, R.B. (2012). “Improved cyberbullying detection using gender information:” Ghent University.
  • Dadvar, M., Trieschnigg, R.B. and Jong, F.M.G. de (2013). “Expert knowledge for automatic detection of bullies in social networks:” TU Delft.
  • Diamanduros, T., Downs, E. and Jenkins, S.J. (2008). “The role of school psychologists in the assessment, prevention, and intervention of cyberbullying:” Psychology in the Schools,Wiley-Blackwell Vol. 45, No. 8, pp. 693–704.
  • Dinakar, K., Reichart, R. and Lieberman, H. (2011). Modeling the Detection of Textual Cyberbullying. The Social Mobile Web, Papers from the 2011 ICWSM Workshop, Barcelona, Catalonia, Spain.
  • Eşsiz, E.S. (2016). Selecting Optimum Feature Subsets With Nature Inspired Algorithms for Cyberbully Detection, Çukurova University.
  • Gülgezen, G. (2009). Stable And Accurate Feature Selection, Istanbul Technical University.
  • Hanchuan Peng, H., Fuhui Long, F. and Ding, C. (2005). “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy:” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp. 1226–1238.
  • Hinduja, S. and Patchin, J.W. (2008). “Cyberbullying: An Exploratory Analysis of Factors Related to Offending and Victimization:” Deviant Behavior, Taylor & Francis Group Vol. 29, No. 2, pp. 129–156.
  • Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. European Conference on Machine Learning, Springer, Berlin, Heidelberg, pp. 171–182.
  • Kontostathis, A. and Kontostathis, A. (2009). ChatCoder: Toward the Tracking and Categorization of Internet Predators. Proc. Text Mining Workshop 2009 Held In Conjunction With The Ninth Siam International Conference On Data Mining (Sdm 2009),.
  • Kowalski, R.M., Limber, S.P. and McCord, A. (2018). “A developmental approach to cyberbullying: Prevalence and protective factors:” Aggression and Violent Behavior,Pergamon.
  • Langos, C. (2012). “Cyberbullying: The Challenge to Define:” Cyberpsychology, Behavior, and Social Networking, Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA Vol. 15, No. 6, pp. 285–289.
  • Li, T.B.Q. (2005). “Cyber-Harassment: A Study of a New Method for an Old Behavior:” Journal of Educational Computing Research,SAGE PublicationsSage CA: Los Angeles, CA Vol. 32, No. 3, pp. 265–277.
  • McHugh, M.L. (2013). “The Chi-square test of independence:” Biochemia Medica,Medicinska naklada Vol. 23, No. 2, pp. 143–149.
  • Nahar, V., Unankard, S., Li, X. and Pang, C. (2012). Sentiment Analysis for Effective Detection of Cyber Bullying. Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications, Springer-Verlag, pp. 767–774.
  • Noviantho, Isa, S.M. and Ashianti, L. (2017). Cyberbullying classification using text mining. 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), IEEE, pp. 241–246.
  • Ozel, S.A., Sarac, E., Akdemir, S. and Aksu, H. (2017). Detection of cyberbullying on social media messages in Turkish. 2017 International Conference on Computer Science and Engineering (UBMK), IEEE, pp. 366–370.
  • Poland, S. (2010). “Cyberbullying Continues to Challenge Educators:” District Administration, Vol. 46, No. 5, p. 55.
  • Rosario, S.F. and Thangadurai, K. (2015). “RELIEF: Feature Selection Approach:” International Journal of Innovative Research and Development, Vol. 4, No. 11.
  • Snakenborg, J., Van Acker, R. and Gable, R.A. (2011). “Cyberbullying: Prevention and Intervention to Protect Our Children and Youth:” Preventing School Failure: Alternative Education for Children and Youth, Vol. 55, No. 2, pp. 88–95.
  • Urbanowicz, R.J., Meeker, M., LaCava, W., Olson, R.S. and Moore, J.H. (2017). “Relief-Based Feature Selection: Introduction and Review:” Journal of Biomedical Informatics,.
  • Wang, J., Shan, G., Duan, X. and Wen, B. (2011). Improved SVM-RFE feature selection method for multi-SVM classifier. 2011 International Conference on Electrical and Control Engineering, IEEE, pp. 1592–1595.
  • Wolak, J., Mitchell, K.J. and Finkelhor, D. (2007). “Does Online Harassment Constitute Bullying? An Exploration of Online Harassment by Known Peers and Online-Only Contacts:” Journal of Adolescent Health, Vol. 41, No. 6, pp. S51–S58.
  • Ybarra, M.L., Diener-West, M. and Leaf, P.J. (2007). “Examining the Overlap in Internet Harassment and School Bullying: Implications for School Intervention:” Journal of Adolescent Health, Vol. 41, No. 6, pp. S42–S50.
  • Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A. and Edwar, L. (2009). Detection of Harassment on Web 2.0. Proceedings of the Content Analysis in the WEB 2.0 (CAW2.0) Workshop at WWW2009, Madrid, Spain, pp. 1–7.
Primary Language en
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-0028-9890
Author: Çiğdem ACI (Primary Author)
Institution: MERSIN UNIVERSITY
Country: Turkey


Orcid: 0000-0002-4631-7834
Author: Eren Çürük
Institution: MERSIN UNIVERSITY
Country: Turkey


Orcid: 0000-0002-2503-0084
Author: Esra Saraç Eşsiz
Institution: ADANA SCIENCE AND TECHNOLOGY UNIVERSITY
Country: Turkey


Dates

Publication Date: October 1, 2019

Bibtex @research article { tuje554417, journal = {Turkish Journal of Engineering}, issn = {}, eissn = {2587-1366}, address = {Murat YAKAR}, year = {2019}, volume = {3}, pages = {168 - 178}, doi = {10.31127/tuje.554417}, title = {AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS}, key = {cite}, author = {ACI, Çiğdem and Çürük, Eren and Eşsiz, Esra Saraç} }
APA ACI, Ç , Çürük, E , Eşsiz, E . (2019). AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS. Turkish Journal of Engineering, 3 (4), 168-178. DOI: 10.31127/tuje.554417
MLA ACI, Ç , Çürük, E , Eşsiz, E . "AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS". Turkish Journal of Engineering 3 (2019): 168-178 <http://dergipark.org.tr/tuje/issue/47586/554417>
Chicago ACI, Ç , Çürük, E , Eşsiz, E . "AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS". Turkish Journal of Engineering 3 (2019): 168-178
RIS TY - JOUR T1 - AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS AU - Çiğdem ACI , Eren Çürük , Esra Saraç Eşsiz Y1 - 2019 PY - 2019 N1 - doi: 10.31127/tuje.554417 DO - 10.31127/tuje.554417 T2 - Turkish Journal of Engineering JF - Journal JO - JOR SP - 168 EP - 178 VL - 3 IS - 4 SN - -2587-1366 M3 - doi: 10.31127/tuje.554417 UR - https://doi.org/10.31127/tuje.554417 Y2 - 2019 ER -
EndNote %0 Turkish Journal of Engineering AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS %A Çiğdem ACI , Eren Çürük , Esra Saraç Eşsiz %T AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS %D 2019 %J Turkish Journal of Engineering %P -2587-1366 %V 3 %N 4 %R doi: 10.31127/tuje.554417 %U 10.31127/tuje.554417
ISNAD ACI, Çiğdem , Çürük, Eren , Eşsiz, Esra Saraç . "AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS". Turkish Journal of Engineering 3 / 4 (October 2019): 168-178. https://doi.org/10.31127/tuje.554417
AMA ACI Ç , Çürük E , Eşsiz E . AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS. TUJE. 2019; 3(4): 168-178.
Vancouver ACI Ç , Çürük E , Eşsiz E . AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS. Turkish Journal of Engineering. 2019; 3(4): 178-168.