Rolling-Type Collaborative Training for False Comment Identification: Enhancing Accuracy through Multi-Characteristic Fusion

This research presents a false comment identification method based on rolling-type collaborative training. False comments pose a significant challenge in online platforms, impacting credibility and user experiences. The proposed method effectively utilizes unlabeled samples to assist model learning and integrates multiple characteristics, including emotion and text representation, to enhance the identification performance. The method involves obtaining comment text and determining its content characteristics, as well as obtaining reviewer information and determining their behavior characteristics. By combining these characteristics, the method performs false comment identification and outputs the identification result. Experimental results show that the proposed method achieves a 3.5% improvement in accuracy compared to traditional methods. The rolling-type collaborative training approach demonstrates the potential to enhance the reliability of comment evaluation systems and combat the spread of false information in online platforms.

Keywords: Comment text content characteristics, False comment identification, Identification performance, Multi-characteristic fusion, Model learning, Rollingtype collaborative training, Reviewer behavior characteristics.