The rapid development of social media has led to an increase in online harassment and offensive speech, posing significant challenges for effective content moderation. Existing automated detection models often exhibit a bias towards predicting offensive speech based on specific vocabulary, which not only compromises model fairness but also potentially exacerbates biases against vulnerable and minority groups. Addressing these issues, this paper proposes a bias self-awareness and data self-iteration framework for mitigating model biases. This framework aims to “giving control back to models: enabling offensive language detection models to autonomously identify and mitigate biases” through bias self-awareness algorithms and self-iterative data augmentation method. Experimental results demonstrate that the proposed framework effectively reduces the false positive rate of models in both in-distribution and out-of-distribution tests, enhances model accuracy and fairness, and shows promising performance improvements in detecting offensive speech on larger-scale datasets.
The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.