Abstract：With the rapid popularity of social media, user-generated content has shown an explosive growth. Against this background, online violent speech has become rampant, causing much concern and posing many challenges to internet language governance. Due to the implicitness and diversity of online violent speech, there are some problems in the automatic detection of those speeches. Firstly, there is a lack of a fine-grained and rich corpus of Chinese online violent speech for the training of automatic detection methods. Secondly, online violent speech adopts implicit expressions, thus weakening the capacity of automatic detection methods. This paper reviews the previous studies on internet language governance and points out the challenging issues. At the same time, a large-scale Chinese online violent speech corpus was constructed to lay the foundation for the detection of Chinese online violent speech. The online violent speech detection methods were proposed on the basis of Dual Contrastive Learning and Emotion-enriched Multi-label and verified in a variety of data sets. This paper argues that Large Language Models should be applied together with background information to detect violent speech more accurately. In addition, the content generated by these models should comply with moral standards of our society. This paper ends with discussions of the impact of Large Language Models on online violent speech detection and suggestions for future research directions.
张冬瑜，卢俊宇，闵昶榮，林鸿飞. 网络暴力言论检测的技术和实践[J]. 语言战略研究, 2024, 9(1): 64-75.
Zhang Dongyu, Lu Junyu, Min Changrong and Lin Hongfei. The Technologies and Practices of Detecting Online Violent Speech. , 2024, 9(1): 64-75.
