Chinese Cyberbullying Detection: Dataset, Method, and Validation
This addresses cyberbullying detection in Chinese social media by providing a first-of-its-kind incident-based dataset, though it is incremental as it adapts existing methods to a new domain.
The authors tackled the problem of cyberbullying detection by creating a novel Chinese dataset organized by incidents, resulting in CHNCI with 220,676 comments across 91 incidents, which serves as a benchmark for detection and prediction tasks.
Existing cyberbullying detection benchmarks were organized by the polarity of speech, such as "offensive" and "non-offensive", which were essentially hate speech detection. However, in the real world, cyberbullying often attracted widespread social attention through incidents. To address this problem, we propose a novel annotation method to construct a cyberbullying dataset that organized by incidents. The constructed CHNCI is the first Chinese cyberbullying incident detection dataset, which consists of 220,676 comments in 91 incidents. Specifically, we first combine three cyberbullying detection methods based on explanations generation as an ensemble method to generate the pseudo labels, and then let human annotators judge these labels. Then we propose the evaluation criteria for validating whether it constitutes a cyberbullying incident. Experimental results demonstrate that the constructed dataset can be a benchmark for the tasks of cyberbullying detection and incident prediction. To the best of our knowledge, this is the first study for the Chinese cyberbullying incident detection task.