A Practitioner's Approach to Problem-Solving using AI

Author(s): Sandeep Kumar*, Shashank Awasthi, Nilotpal Pathak, Amit Gupta and Rajesh Pokhariyal

DOI: 10.2174/9789815305364124010010

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

Pp: 130-141 (12)

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Abstract

SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.

Abstract

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.

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