Fast Rumor Detection in Social Networks Through Large Language Models-Based Semantic Enhancement

With the popularity of social media, the rapid spread of rumors has brought great challenges to social stability and personal safety. Therefore, it is of great significance to develop an efficient and accurate rumor detection method. This study first reviews this research status of social network rumor detection, analyzes the advantages and disadvantages of traditional methods and deep learning-based methods, and then completes network language feature extraction based on Transformer, uses KIMI large language model (LLM) for semantic enhancement, and then completes the establishment of semantic feature fusion module. Finally, KIMI and GPT are integrated to construct a hybrid LLM for fast detection of Internet rumors. In order to verify the effectiveness of this method, this study obtains data sets from MicroBlog community management center, Twitter and TikTok for experiments. The experimental results show that the semantic enhancement technology based on the mixed LLM performs well in the rumor detection task, which is significantly better than the single LLM. In addition, we also discuss the effect of semantic enhancement strategy on model performance, and find that this strategy can further improve the accuracy and robustness of the model. The experimental results verify the effectiveness and practicability of this method, and provide a new idea and method for the rapid detection of social network rumors.