Abstract
The paper deals with the issue of hate speech and radicalization. Oftentimes, they are spread by means of social media. Twitter lets one express their views in a relatively anonymous way; however, it seems to be a simple, yet effective tool for disseminating offensive or radical contents, too. The paper proposes an effective solution which applies machine learning for detecting signs of radicalization and hate speech in Twitter posts. The authors decided to use the Polish language, which due to the level of its complexity is known to pose a challenge for automated sentiment analysis. The authors also needed to create their own dataset of posts containing hate speech, as prior to the experiment, there existed no such datasets in the language. In the paper, the underlying technologies are first presented, then the course of experiment is described and the final conclusions are given thereafter.
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Kuchczyński, M., Pawlicka, A., Pawlicki, M., Choraś, M. (2022). Using Machine Learning to Detect the Signs of Radicalization and Hate Speech on Twitter. In: Choraś, M., Choraś, R.S., Kurzyński, M., Trajdos, P., Pejaś, J., Hyla, T. (eds) Progress in Image Processing, Pattern Recognition and Communication Systems. CORES IP&C ACS 2021 2021 2021. Lecture Notes in Networks and Systems, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-81523-3_21
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