Abstract
Aggressive text detection in social networks allows to identify offenses and misbehavior, and leverages tasks such as cyberbullying detection. We propose to automatically map a document with an aggressiveness score (thus treating aggressive text detection as a regression problem) and explore different approaches for this purpose. These include lexicon-based, supervised, fuzzy, and statistical approaches. We test the different methods over a dataset extracted from Twitter and compare them against human evaluation. Our results favor approaches that consider several features (particularly the presence of swear or profane words).
This is a preview of subscription content, log in via an institution to check access.
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
Análise de sentimentos no Twitter utilizando SentiWordNet, Proposta de Trabalho de Graduação (2011)
Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical report, Center for Research in Psychophysiology, University of Florida (1999)
Campbell, M.A.: Cyber bullying: An old problem in a new guise? Australian Journal of Guidance and Counselling 15(1), 68–76 (2005)
Dadvar, M., de Jong, F.M.G., Ordelman, R.J.F., Trieschnigg, R.B.: Improved cyberbullying detection using gender information. In: Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012), Ghent, pp. 23–26. University of Ghent (2012)
Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: The Social Mobile Web (2011)
Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies 11(4), 441–456 (2010)
Dorothy, L.: Espelage and Susan M Swearer. Research on school bullying and victimization: What have we learned and where do we go from here? School Psychology Review (2003)
Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22(2), 110–125 (2006)
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Nahar, V., Xue, L., Chaoyi, P.: An effective approach for cyberbullying detection. Communications in Information Science and Management Engineering (2012)
Sood, S., Antin, J., Churchill, E.: Using crowdsourcing to improve profanity detection. In: AAAI Spring Symposium Series, pp. 69–74 (2012)
Sticca, F., Perren, S.: Is cyberbullying worse than traditional bullying? Examining the differential roles of medium, publicity, and anonymity for the perceived severity of bullying. Journal of Youth and Adolescence, pp. 1–12 (2012)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
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
Del Bosque, L.P., Garza, S.E. (2014). Aggressive Text Detection for Cyberbullying. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Human-Inspired Computing and Its Applications. MICAI 2014. Lecture Notes in Computer Science(), vol 8856. Springer, Cham. https://doi.org/10.1007/978-3-319-13647-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-13647-9_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13646-2
Online ISBN: 978-3-319-13647-9
eBook Packages: Computer ScienceComputer Science (R0)