Fuzzy Based Genetic Operators for Cyber Bullying Detection Using Social Network Data

Authors(2) :-M. Devi, M. Chitra Devi

Social media getting more and more popular in our day today life. By the popularity of the social media affects the people who involving into it. This makes the technology to work or to feel smarter and makes us lazier. On resulting to this robust and discriminative numerical representation learning of text messages is a critical issue. Hence here we propose a learning method to tackle this issue which is named as Semantic Enhanced Marginalized Denoising Auto Encoder (smsda). Semantic extension of the popular deep learning model stacked denoising auto encoder plays a major role in this method whereas semantic extension consists of semantic dropout noise and sparsity constraints. The semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bullying information and learn a robust and discriminative representation of text. Comprehensive experiments on two public cyber bullying corpora (Twitter and myspace) are conducted, and the results show that our proposed approaches outperform other baseline text representation learning methods

Authors and Affiliations

M. Devi
Research Scholar, Department of Computer Science, Sakthi Arts and Science College For Women, Oddanchatram, Tamil Nadu, India
M. Chitra Devi
PG Head & Associate Professor, Department of Computer Science, Sakthi Arts and Science College For Women, Oddanchatram, Tamil Nadu, India

Semantic Enhanced Marginalized Denoising Auto-Encoder, cyberbullying.

  1. A. M. Kaplan and M. Haenlein, “Users of the world, unite! The challenges and opportunities of social media,” Business horizons, vol. 53, no. 1, pp. 59–68, 2010.
  2. R. M. Kowalski, G. W. Giumetti, A. N.Schroeder, and M. R.Lattanner, “Bullying in the digital age: A critical review and metaanalysis of cyberbullying research among youth.” 2014.
  3. M. Ybarra, “Trends in technology-based sexual and non-sexual aggression over time And linkagest non technology aggression,”National Summiton Interpersonal Violence and Abuse Across the Lifespan: Forging a Shared Agenda, 2010.
  4. B. K. Biggs, J. M. Nelson, and M. L. Sampilo, “Peer relations in the anxiety– depression link: Test of a mediation model,”Anxiety, Stress, & Coping, vol. 23, no. 4, pp. 431–447, 2010.
  5. S. R. Jimerson, S. M. Swearer, and D. L. Espelage, Handbook of bullying in schools: An international perspective. Rout ledge/Taylor & Francis Group, 2010.
  6. G. Gini and T. Pozzoli, “Association between bullying and psychosomatic problems: A meta-analysis,” Pediatrics, vol.123, no. 3, pp. 1059–1065, 2009.
  7. A. Kontostathis, L. Edwards, and A. Leatherman, “Text mining and cybercrime,” Text Mining: Applications and Theory. John Wiley & Sons, Ltd, Chichester, UK, 2010.
  8. J.-M. Xu, K.-S. Jun, X. Zhu, and A. Bellmore, “Learning from bullying traces in social media,” in Proceedings of the 2012 conferenceof the North American chapter of the association for computational linguistics: Human language technologies. Association for Computational Linguistics, 2012, pp. 656–666.
  9. Q. Huang, V. K. Singh, and P. K. Atrey, “Cyber bullying detection using social and textual analysis,” in Proceedings of the 3rd International Workshop on Socially-Aware Multimedia. ACM, 2014, pp.3–6.
  10. D. Yin, Z. Xue, L. Hong, B. D. Davison, A. Kontostathis, and L. Edwards, “Detection of harassment on web 2.0,” Proceedings of the Content Analysis in the WEB, vol. 2, pp. 1–7, 2009.
  11. K. Dinakar, R. Reichart, and H. Lieberman, “Modeling the detection of textual cyberbullying.” in The Social Mobile Web, 2011.
  12. V. Nahar, X. Li, and C. Pang, “An effective approach for cyberbullying detection,” Communications in Information Science and Management Engineering, 2012.
  13. M. Dadvar, F. de Jong, R. Ordelman, and R. Trieschnigg, “Improved cyberbullying detection using gender information,” in Proceedings of the 12th - Dutch-Belgian Information Retrieval Workshop (DIR2012). Ghent, Belgium: ACM, 2012.
  14. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” The Journal of Machine Learning Research, vol. 11, pp. 3371–3408, 2010.
  15. P. Baldi, “Autoencoders, unsupervised learning, and deep architectures,” Unsupervised and Transfer Learning Challenges in Machine Learning, Volume 7, p. 43, 2012.

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 437-444
Manuscript Number : CSEIT1833243
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

M. Devi, M. Chitra Devi, "Fuzzy Based Genetic Operators for Cyber Bullying Detection Using Social Network Data", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.437-444, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833243

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