The "on hold" event of IEEE Transactions on Intelligent Vehicles (TIV) is a typical case of misunderstanding of how a journal runs, and misleading public opinions from online media seriously harm a journal. Public opinions on this kind of case had never been investigated. The paper proposes a multidimensional quantitative regression framework (MQRF) that demonstrates how to quantify and predict public opinion influences for such cases through the collaboration of large language model and small model. The framework leverages large language models’ comprehensive analytical capabilities for contextual understanding while employing specialized small models for precise time-series prediction, creating a synergistic approach that significantly outperforms single-model solutions. We analyze the evolving relationships among public opinions and examine how these changes prompt the journal to disclose and clarify its operational practices. Public misunderstandings cause lasting damage to the journal. Understanding how different types of papers run in the journal can eliminate misunderstanding though with a time lag. Through in-depth analysis of the TIV event and comparative experiments with six small models (Linear, DLinear, NLinear, TimesNet, Transformer, and PatchTST) and six large language models (ChatGPT-4.5, Claude3.7sonnet, Grok3, Deepseek-R1, Doubao, and Qwen2.5-72B-Instruct), the effectiveness of the MQRF is verified.