Recently, publishers of offensive comments are increasingly employing strategies such as metaphors, abbreviations, and homophones to obscure the aggressive nature of their comments. These strategies pose a significant challenge for existing detection models. At present, many studies mainly focused on the detection of explicit offensive speech, and there were few studies on implicit offensive speech. Our research aims to analyze implicit offensive speech on Chinese social platforms and achieve high detection performance. Firstly, we have collected data from one of the largest Chinese social networking platforms, Weibo, and constructed the first Chinese implicit offensive speech dataset, which contains 54,714 comments. Subsequently, we introduce Enhanced-BERT-Mate-Ambiguity (EBMA), a novel fuzzy semantic interpretation framework that leverages BERT and knowledge graphs. Specifically, this model detects implicit offensive speech by extracting semantic, emotional, metaphorical, and ambiguity features. Finally, extensive experiments were conducted, including comparison tests, robustness tests, and ablation studies, to validate our approach. We tested our model against state-of-the-art models in the field, and an accuracy of 95.83% and an F1-score of 95.52% confirmed its best performance. The performance of our model is visually illustrated through visualization. Moreover, we provide an analysis of error cases to explore the limitations of our model.
Liu et al. (Fri,) studied this question.