Email this article A Study to Learn Robust and Discriminative Representation to Tackle Cyber bullying Detection
Abstract
We build up another content portrayal display in view of a variation of SDA: marginalized stacked denoising autoencoders (mSDA), which receives straight rather than nonlinear projection to quicken preparing and minimizes endless clamor dispersion so as to take in more strong portrayals. We use semantic data to grow mSDA and create Semantic-improved Marginalized Stacked Denoising Autoencoders (smSDA). The semantic data comprises of harassing words. A programmed extraction of tormenting words in view of word embeddings is proposed so that the included human work can be diminished. Amid preparing of smSDA, we endeavor to reproduce bullying highlights from other typical words by finding the idle structure, i.e. connection, amongst tormenting and typical words. The instinct behind this thought is that some harassing messages don't contain bullying words.
References
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.
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.
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.
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.
S. R. Jimerson, S. M. Swearer, and D. L. Espelage, Handbook of bullying in schools: An international perspective. Routledge/Taylor & Francis Group, 2010.
G. Gini and T. Pozzoli, “Association between bullying and psychosomatic problems: A meta-analysis,” Pediatrics, vol. 123, no. 3,pp. 1059–1065, 2009.
A. Kontostathis, L. Edwards, and A. Leatherman, “Text mining and cybercrime,” Text Mining: Applications and Theory. John Wiley & Sons, Ltd, Chichester, UK, 2010.
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.
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.
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.
K. Dinakar, R. Reichart, and H. Lieberman, “Modeling the detection of textual cyberbullying.” in The Social Mobile Web, 2011.
V. Nahar, X. Li, and C. Pang, “An effective approach for cyberbullying detection,” Communications in Information Science and Management Engineering, 2012.
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.
M. Dadvar, D. Trieschnigg, R. Ordelman, and F. de Jong, “Improving cyberbullying detection with user context,” in Advances in Information Retrieval. Springer, 2013, pp. 693–696.
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.
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