Abstract
Current approaches on cyberbullying detection are mostly static: they are unable to handle noisy, imbalanced or streaming data efficiently. Existing studies on cyberbullying detection are mainly supervised learning approaches, assuming data is sufficiently pre-labelled. However this is impractical in the real-world situation where only a small number of labels are available in streaming data. In this paper, we propose a semi-supervised leaning approach that will augment training data samples and apply a fuzzy SVM algorithm. The augmented training technique automatically extracts and enlarges training set from the unlabelled streaming text, while learning is conducted by utilising a very small training set provided as an initial input. The experimental results indicate that the proposed augmented approach outperformed all other methods, and is suitable in the real-world situations, where sufficiently labelled instances are not available for training. For the proposed fuzzy SVM approach we handle complex and multidimensional data generated by streaming text, where the importance of features are discriminated for the decision function. The evaluation conducted on different experimental scenarios indicates the superiority of the proposed fuzzy SVM against all other methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, J.M., Burchfiel, B., Zhu, X., Bellmore, A.: An examination of regret in bullying tweets. In: The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 697–702 (2013)
Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 693–696. Springer, Heidelberg (2013)
Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: AAAI Conference on Weblogs and Social Media, pp. 11–17 (2011)
Nahar, V., Unankard, S., Li, X., Pang, C.: Sentiment analysis for effective detection of cyber bullying. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 767–774. Springer, Heidelberg (2012)
Yin, D., Xue, Z., Hong, L., Davisoni, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on web 2.0. In: Content Analysis in the Web 2.0 Workshop at WWW (2009)
Zhang, Y., Li, X., Orlowska, M.: One-class classification of text streams with concept drift. In: ICDMW, pp. 116–125 (2008)
Nahar, V., Li, X., Pang, C., Zhang, Y.: Cyberbullying detection based on text-stream classification. In: AusDM (2013) (in press)
Zhang, T.: Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: ICML, pp. 919–926. ACM (2004)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Zhang, D.Q., Chen, S., Pan, Z.S., Tan, K.R.: Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation. 4, 2189–2192 (2003)
Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine 32, 37–50 (2004)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Transaction on Fuzzy Systems 1, 98–110 (1993)
Wong, C.C., Chen, C.C., Yeh, S.L.: K-means-based fuzzy classifier design. 1, 48–52 (2000)
Gröll, L., Jäkel, J.: A new convergence proof of fuzzy c-means. 13, 717–720 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nahar, V., Al-Maskari, S., Li, X., Pang, C. (2014). Semi-supervised Learning for Cyberbullying Detection in Social Networks. In: Wang, H., Sharaf, M.A. (eds) Databases Theory and Applications. ADC 2014. Lecture Notes in Computer Science, vol 8506. Springer, Cham. https://doi.org/10.1007/978-3-319-08608-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-08608-8_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08607-1
Online ISBN: 978-3-319-08608-8
eBook Packages: Computer ScienceComputer Science (R0)