1. 1. M. Ybarra, “Trends in technology-based sexual and non-sexual aggression over time and linkages to nontechnology aggression,” National Summit on Interpersonal Violence and Abuse Across the Lifespan: Forging a Shared Agenda, 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. J.-M. Xu, K.-S. Jun, X. Zhu, and A. Bellmore, “Learning from bullying traces in social media, ” in Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies. Association for Computational Linguistics, 2012, pp. 656–666.
   4. 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.
   5. 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
   6. K. Dinakar, R. Reichart, and H. Lieberman, “Modeling the detection of textual cyberbullying.” In The Social Mobile Web, 2011.
   7. V. Nahar, X. Li, and C. Pang, “An effective approach for cyberbullying detection,” Communications in Information Science and Management Engineering, 2012.
   8. M. Dadvar, D. Trieschnigg, R. Ordelman, and F. de Jong, “Improving cyberbullying detection with user context,” inAdvancesin Information Retrieval. Springer, 2013, pp. 693–696.
   9. 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.
   10. P. Baldi, “Autoencoders, unsupervised learning, and deep architectures,” Unsupervised and Transfer Learning Challenges in Machine Learning, Volume 7, p. 43, 2012.
   11. M. Chen, Z. Xu, K. Weinberger, and F. Sha, “Marginalized denoising autoencoders for domain adaptation,” arXiv preprint arXiv:1206.4683, 2012.
   12. T. K. Landauer, P. W. Foltz, and D. Laham, “An introduction to latent semantic analysis,” Discourse processes, vol. 25, no. 2-3, pp. 259–284, 1998
   13. T. L. Griffiths and M. Steyvers, “Finding scientific topics,” Proceedings of the National academy of Sciences of the United States of America, vol. 101, no. Suppl 1, pp. 5228–5235, 2004
   14. T. Hofmann, “Unsupervised learning by probabilistic latent semantic analysis,” Machine learning, vol. 42, no. 1-2, pp. 177–196, 2001.
   15. D.M.Blei,A.Y.Ng, and M.I.Jordan ,“Latent dirchlent allocation,” the Journal of machine Learning research, vol. 3, pp. 993–1022, 2003.
   16. Ptaszynski, F. Masui, Y. Kimura, R. Rzepka, and K. Araki, “Brute force works best against bullying,” in Proceedings of IJCAI 2015 Joint Workshop on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization. ACM, 2015.
   17. R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society. Series B (Methodological), pp. 267–288, 1996.
   18. J.FanandR.Li,“Variable selection via non concave penalized likelihood and its oracle properties,” Journal of the American statistical Association, vol. 96, no. 456, pp. 1348–1360, 2001.
   19. T.H.DatandC.Guan,“Feature selection based on fisher ratio and mutual information analyses for robust brain computer interface,” in Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, vol. 1. IEEE, 2007, pp. I– 337.