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Modern Approaches to Detecting and Classifying Toxic Comments Using Neural Networks

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Abstract—

The rising popularity of online platforms on which users communicate with each other, share opinions about various events, and leave comments has spurred on the development of natural language processing algorithms. Content moderation requires analyzing tens of millions of messages published by users of a given social network daily in real time, in order to prevent the spread of various illegal or offensive information, threats, and other types of toxic comments. Of course, such a large amount of data can be processed quickly enough only automatically. That leads to the problem of teaching computers to “understand” human written speech, which is nontrivial even if understand here means nothing more than classify. The rapid evolution of machine learning technologies has led to ubiquitous implementation of new algorithms. With the use of deep learning technologies, we are now able to quite successfully solve many problems that had for years been considered almost impossible. This article considers algorithms constructed using deep learning technologies and neural networks that solve the problem of detecting and classifying toxic comments. In addition, the article presents the results of testing both the developed algorithms and an ensemble of all considered algorithms on a large training set collected and tagged by Google and Jigsaw.

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Notes

  1. It was eventually decided to forgo this operation due to the excessive processing time required to perform it on a corpus of this size.

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Authors and Affiliations

  1. Demidov Yaroslavl State University, 150003, Yaroslavl, Russia

    S. V. Morzhov

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  1. S. V. Morzhov
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Correspondence to S. V. Morzhov.

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Translated by A. Ovchinnikova

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Morzhov, S.V. Modern Approaches to Detecting and Classifying Toxic Comments Using Neural Networks. Aut. Control Comp. Sci. 55, 607–616 (2021). https://doi.org/10.3103/S0146411621070117

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