Abstract
Nowadays, users are increasing their participation in the Internet and, particularly, in social news websites. In these webs, users can comment diverse stories or other users’ comments. In this paper we propose a new method based for filtering trolling comments. To this end, we extract several features from the text of the comments, specifically, we use a combination of statistical, syntactic and opinion features. These features are used to train several machine learning techniques. Since the number of comments is very high and the process of labelling tedious, we use a collective learning approach to reduce the labelling efforts of classic supervised approaches. We validate our approach with data from ‘Menéame’, a popular Spanish social news site.
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de-la-Peña-Sordo, J., Santos, I., Pastor-López, I., Bringas, P.G. (2013). Filtering Trolling Comments through Collective Classification. In: Lopez, J., Huang, X., Sandhu, R. (eds) Network and System Security. NSS 2013. Lecture Notes in Computer Science, vol 7873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38631-2_60
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DOI: https://doi.org/10.1007/978-3-642-38631-2_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38630-5
Online ISBN: 978-3-642-38631-2
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