Abstract:

With the rapid development of the Internet and mobile Internet technologies, more and more people are eager to obtain information and express their views and opinions on social networks. However, in recent years, social networks have been flooded with an increasing amount of offensive language and other undesirable comments, leading to the proliferation of online violence. Currently, research on offensive language detection is mostly concentrated in the English language field, with few studies focused on offensive language detection in Chinese. To address this issue, this thesis collected a large amount of tweet data from the Sina Weibo platform and annotated the data according to established rules to construct a Chinese offensive language dataset. Then, statistical features, including sentiment features, content features, and communication features, were extracted. Finally, a multi-task learning-based offensive language detection model was constructed. The auxiliary task of sentiment analysis was introduced to improve the detection performance of the model by leveraging the high correlation between the two tasks. Experimental results show that the model proposed in this thesis outperforms other commonly used detection methods for offensive language detection. The research provides methods and ideas for future offensive language detection on social networks.

Key words: offensive language, multi-task learning, social networks, deep learning