The Cross-Cultural Sports Sentiment (CCSS) dataset is a multimodal resource for sentiment analysis and cultural communication in competitive sports. It integrates multi-source, multilingual data from global social media platforms such as Sina Weibo, Reddit, Twitter, Facebook, and YouTube, together with reports from 117 domestic and international news outlets, resulting in a multimodal corpus comprising over 9955 comment interactions, 886 news articles, and 876 min of video. Built around 60 key competitive events involving twelve elite athletes from China and abroad, and collected within time windows centered on athletes’ key competitive events, the dataset captures the temporal evolution of public opinion and emotion after events; by incorporating geo-cultural factors of competitions, it reveals the cross-cultural diffusion mechanisms of sporting spirit. The dataset provides a benchmark platform for cross-modal semantic understanding and offers a standardized evaluation framework for research on multimodal artificial intelligence (AI) in cross-cultural sentiment computation and modeling of sporting spirit communication.

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 Pre-trained 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.