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Open Access

CCSS: A Multimodal Dataset for Cross-Cultural Sentiment Analysis and Sporting Spirit Communication in Competitive Sports

School of Journalism and Communication, Beijing Sport University, Beijing 100084, China
School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, China
State Key Laboratory of Media Integration and Communication, Communication University of China, Beijing 100024, China
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Abstract

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.

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Journal of Social Computing
Pages 255-268

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Cite this article:
Xue W, Luo T, Wei Q, et al. CCSS: A Multimodal Dataset for Cross-Cultural Sentiment Analysis and Sporting Spirit Communication in Competitive Sports. Journal of Social Computing, 2026, 7(3): 255-268. https://doi.org/10.23919/JSC.2026.0009

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Received: 04 December 2025
Revised: 11 January 2026
Accepted: 11 February 2026
Published: 14 September 2026
© The author(s) 2026.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).