Profiling hate speech spreaders on Twitter using BERT pre-trained model and neural network

Yutong Sun; Han Xiao

doi:10.1117/12.2660811

16 December 2022 Profiling hate speech spreaders on Twitter using BERT pre-trained model and neural network

Yutong Sun, Han Xiao

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 125005X (2022) https://doi.org/10.1117/12.2660811
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

The profiling hate speech readers task is to determine whether the author of a given group of English or Spanish tweets posted on twitter spreads hate speech. At present, the automatic recognition of hate, irony and false speech is the top priority in the field of author profiling, which has practical significance. This paper proposes a deep learning model based on Bert pre-trained model to extract deep text semantic features, and use counting method to obtain stylistic features. Finally, hate speech profiling is regarded as a binary classification task, and full connected neural network is used for classification prediction. The accuracy is 71.5% on the English data set and 78% on the Spanish test set.

Citation Download Citation

Yutong Sun and Han Xiao "Profiling hate speech spreaders on Twitter using BERT pre-trained model and neural network", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 125005X (16 December 2022); https://doi.org/10.1117/12.2660811

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