Chinese cyber-violent Speech Detection and Analysis Based on Pre-trained Model

Cyber-violent speech is prevalent on Chinese social platforms today, and traditional manual moderation by platform administrators is no longer effective in detecting and analyzing it. Therefore, the use of artificial intelligence technologies like natural language processing for automated detection on the Internet is an essential requirement to promptly prevent the spread of cyber-violent speech. Due to the covert and diverse nature of cyber-violent speech, existing models have shown unsatisfactory performance in detecting implicitly expressed violent speech. This paper proposes a violence speech detection method based on BERT and Hanyu Pinyin and emotion assistance, and its effectiveness and advancement are validated on multiple datasets. Subsequently, the experimental results are analyzed to summarize the characteristics of Chinese violent speech, facilitating further development in violence speech detection efforts in the future.