Finnish Hate-Speech Detection on Social Media Using CNN and FinBERT

Abstract

There has been a lot of research in identifying hate posts from social media because of their detrimental effects on both individuals and society. The majority of this research has concentrated on English, although one notices the emergence of multilingual detection tools such as multilingual-BERT (mBERT). However, there is a lack of hate speech datasets compared to English, and a multilingual pre-trained model often contains fewer tokens for other languages. This paper attempts to contribute to hate speech identification in Finnish by constructing a new hate speech dataset that is collected from a popular forum (Suomi24). Furthermore, we have experimented with FinBERT pre-trained model performance for Finnish hate speech detection compared to state-of-the-art mBERT and other practices. In addition, we tested the performance of FinBERT compared to fastText as embedding, which employed with Convolution Neural Network (CNN). Our results showed that FinBERT yields a 91.7% accuracy and 90.8% F1 score value, which outperforms all state-of-art models, including multilingual-BERT and CNN.

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