Abstract
The detection of aggressive behavior in online discussion communities is of great interest, due to the large number of users, especially of young age, who are frequently exposed to such behaviors in social networks. Research on cyberbullying prevention focuses on the detection of potentially harmful messages and the development of intelligent systems for the identification of verbal aggressiveness expressed with insults and threats. Text mining techniques are among the most promising tools used so far in the field of aggressive sentiments detection in short texts, such as comments, reviews, tweets etc. This article presents a novel approach which employs sentiment analysis at message level, but considers the whole communication thread (i.e., users discussions) as the context of the aggressive behavior. The suggested approach is able to detect aggressive, inappropriate or antisocial behavior, under the prism of the discussion context. Key aspects of the approach are the monitoring and analysis of the most recently published comments, and the application of text classification techniques for detecting whether an aggressive action actually emerges in a discussion thread. Thorough experimental validation of the suggested approach in a dataset for cyberbullying detection tasks demonstrates its applicability and advantages compared to other approaches.
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Notes
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Rule-based classifier, called BullyTracer, was used in [26] in the same dataset that we use in this work. However, any other classification method can be applied in this step.
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The original dataset and the datasets we used in the current research can be downloaded from: https://goo.gl/wPrU2n.
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The RBFClassifier implementation for Weka has been used.
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The LibSVM implementation of Weka with default parameters.
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Attribute Selected Classifier with PCA as attribute selection method and RBF classifier as classification method, was used.
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Ventirozos, F.K., Varlamis, I., Tsatsaronis, G. (2018). Detecting Aggressive Behavior in Discussion Threads Using Text Mining. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_31
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