Abstract
Abusive language online has become a growing social issue in our age of social media. Given the massive amounts of data being generated daily on social platforms, manually detecting and regulating such behavior has become unfeasible, so automatic solutions are necessary, and tasks related to identifying abusive language, in its various forms, from hate speech to bullying, have come into the focus of the natural language processing research community. In this paper, we focus on two subtypes of abusive language: aggressive language and offensive language, for which we implement a deep learning model based on convolutional neural networks. We further propose a new approach using transfer learning to boost performance of abusive language detection by leveraging data annotated with a different type of label, related to sentiment. We show how transferring knowledge between these tasks affects performance of detecting abusive language, offering insights into how these tasks are related, and how the more traditional task of sentiment analysis can be leveraged to help with solving the newer and less data rich task of abusive language detection.
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Uban, AS., Dinu, L.P. (2019). On Transfer Learning for Detecting Abusive Language Online. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_57
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