Harmfulness metrics in digital twins of social network rumors detection in cloud computing environment

Social network rumor harm metric is a task to score the harm caused by a rumor by analyzing the spreading range of the rumor, the users affected, the repercussions caused, etc., and then the harm caused by the rumor. Rumor hazard metric models can help rumor detection digital twins to understand and analyze user behaviors and assist social network network managers to make more informed decisions. However, there is a lack of models that can quantify the harm of rumors and automated harm metric models in rumor detection digital twins. To address this issue, this paper proposes an innovative social network rumor harm metric based on rumor propagation knowledge and a large language model (LLM), RSK-T5. The method first completes the joint task of rumor comment stance detection and sentiment analysis to capture critical features of rumor propagation. Then, this knowledge is used in the pre-training process of LLM to improve the model's understanding of rumor propagation patterns. Finally, the fine-tuning phase focuses on the hazard metrics task to improve the generalization energy. We compare with some existing variants of rumor detection methods, and experimental results demonstrate that RSK-T5 achieves the lowest MSE scores on three well-known rumor detection datasets. The ablative learning work demonstrates the effectiveness of RSK-T5's knowledge of two rumor spreads.