Hate Speech Detection in Thai Social Media with Ordinal-Imbalanced Text Classification

Abstract

Cyberbullying has become a serious problem in Thai social media. For example, some Thai people posted hate speeches on Myanmar workers in Thailand during the COVID-19 pandemic, which might elevate hate crime. It is imperative and urgent to detect cyberbullying on Thai social media. The task is a text classification problem. Moreover, hate speeches contain the order of severity levels, but many pieces of work did not consider this point in the model. Therefore, we developed a Thai hate-speech classification method with various loss functions to detect such hate speeches accurately. We evaluated them on a corpus of ordinal-imbalanced Thai text. The evaluated outcomes indicated that the best---in terms of F1-score--model was the model with a loss function of a hybrid between an Ordinal regression loss function and Pearson correlation coefficients (common in similarity function). It yielded an average F1-score of 78.38%--0.88% significantly higher than the score achieved by a conventional loss function--and an average mean squared error of 0.2478--5.49% relative improvement. Thus, the proposed hybrid loss function improved the efficiency of the model.