Flexible inference for cyberbully incident detection

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Abstract

We study detection of cyberbully incidents in online social networks, focusing on session level analysis. We propose several variants of a customized convolutional neural networks (CNN) approach, which processes users’ comments largely independently in the front-end layers, but while also accounting for possible conversational patterns. The front-end layer’s outputs are then combined by one of our designed output layers – namely by either a max layer or by a novel sorting layer, proposed here. Our CNN models outperform existing baselines and are able to achieve classification accuracy of up to 84.29% for cyberbullying and 83.08% for cyberaggression.

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APA

Zhong, H., Miller, D. J., & Squicciarini, A. (2019). Flexible inference for cyberbully incident detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11053 LNAI, pp. 356–371). Springer Verlag. https://doi.org/10.1007/978-3-030-10997-4_22

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