Abstract
The Web has evolved over the years and, now, not only the administrators of a site generate content. Users of a website can express themselves showing their feelings or opinions. This fact has led to negative side effects: sometimes the content generated is inappropriate. Frequently, this content is authored by troll users who deliberately seek controversy. In this paper we propose a new method to detect trolling comments in social news websites. To this end, we extract a combination of statistical, syntactic and opinion features from the user comments. Since this troll phenomenon is quite common in the web, we propose a novel experimental setup for our anomaly detection method: considering troll comments as base model (normal behaviour: ‘normality’). We evaluate our approach with data from ‘Menéame’, a popular Spanish social news site, showing that our method can obtain high rates whilst minimising the labelling task.
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de-la-Peña-Sordo, J., Pastor-López, I., Ugarte-Pedrero, X., Santos, I., Bringas, P.G. (2014). Anomalous User Comment Detection in Social News Websites. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_51
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DOI: https://doi.org/10.1007/978-3-319-07995-0_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
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