Research on Cyberbullying Text Detection Model Based on SHAP Explanations Tool
Aiming to quickly identify whether text content in social media was cyberbullying text,a cy-berbullying text detection model based on RoBERTa-BiGRU was proposed.Firstly,the pretrained Ro-BERTa was used to extract semantic features of the text in the model,and BiGRU was utilized for com-prehensively feature extraction.Secondly,the classification performance of the RoBERTa-BiGRU classifi-cation model was evaluated on the Cyberbullying dataset CB-tweets.Finally,the SHAP interpretation tool was introduced to compare and analyze the key features and baseline values identified by RoBERTa-BiG-RU model from both global and local dimensions.Experimental results showed that RoBERTa-BiGRU model had higher classification accuracy.It was found that the keywords calculated by RoBERTa-BiGRU on Age,Ethnicity,Gender,and Religion categories matched the labels of that category by using inter-pretable tool.However,the keywords found on Other CB and Not CB categories were mostly rare charac-ters and ligatures,indicating that the model did not truly understand the inherent feature differences be-tween Other CB and Not CB categories.
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