Mimicking the Mavens: Agent-Based Opinion Synthesis and Emotion Prediction for Social Media Influencers

Understanding influencers' perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems, enabling more informed decision-making. However, this task is inherently challenging due to the unstructured, context-sensitive, and heterogeneous nature of online communication. To address these challenges, we propose a novel intelligent computational framework, Multi-domain Opinion Leader Agents Emotion Prediction (MOAEP). Our framework comprises three key components: (1) An Automatic Question Generation (AQG) module employing “Who, What, Where, When, Why, and How” (5W1H) questioning to systematically explore topic dimensions; (2) A Multi-domain Opinion Leader Agents (MOA) module that integrates enhanced Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to produce domain-specific responses; and (3) An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses, enabling proactive social computing analysis that surpasses conventional post-event methods. Experimental results demonstrate the framework's efficacy: the AQG module generates high-fidelity outputs, while the influencer agents maintain consistent performance, achieving an average “Generative Pretrained Transformer 4” (GPT-4) evaluation score of 6.85 (on a 0–10 scale) across multiple dimensions. In a social media conflict case study, “Russia-Ukraine War”, our framework successfully predicts key influencers' perspectives and aligns emotional forecasts with observed real-world sentiment trends. These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.