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An explainable dual-attention transformer for predicting the sociocultural impact of global sports events
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  • Published: 09 March 2026

An explainable dual-attention transformer for predicting the sociocultural impact of global sports events

  • Wenzheng Chen1,2,
  • Syed Kamaruzaman Bin Syed Ali3,
  • Hutkemri Zulnaidi4,
  • Gang He1,
  • Guanglei Yang1,
  • Jinsong Li1 &
  • …
  • Changhu Xiang1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Complex networks
  • Cultural and media studies
  • Mathematics and computing
  • Science, technology and society

Abstract

Existing research on sociocultural analytics in global sports has primarily relied on traditional statistical and regression-based approaches that capture limited linear relationships among cultural, economic, and sentiment-related variables. However, the societal influence of international sporting events emerges from complex, nonlinear interactions that require more expressive modeling frameworks. This study proposes a Sociocultural Dual-Attention TabTransformer (SC-DATransformer) to predict a continuous Social Impact Score, defined as a normalized composite index derived from validated indicators including cultural engagement, diversity, gender representation, public sentiment, sustainability, and media coverage. The empirical analysis is conducted on a structured dataset comprising approximately 70,000 event-level observations described by 21 variables, covering multiple global competitions across regions and years. To enhance representation quality and reduce redundancy, feature engineering and selection techniques such as mutual information and dimensionality reduction are employed. Model performance is evaluated against established baselines using complementary regression metrics, supported by 95% bootstrap confidence intervals and Diebold–Mariano statistical testing. Within the experimental setting, the proposed model demonstrates improved predictive consistency and lower error magnitudes relative to baseline approaches. Explainable AI analysis further provides transparent insight into dominant sociocultural drivers influencing impact estimation.

Data availability

The dataset is freely available at: [https://github.com/VisionLangAI/Sports-Event-Analysis](https:/github.com/VisionLangAI/Sports-Event-Analysis).

Code availability

The code is freely available at: https://github.com/ChenWenzheng83/Sports-Analysis.

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Funding

The study was supported by“Sichuan Provincial Key Research Base of Philosophy and Social Sciences — Tianfu International Sports Events Research Center (Grant No. YJY2021Z01)”.

Author information

Authors and Affiliations

  1. Physical Education Teaching Department, Chengdu Aeronautic Polytechnic University, Chengdu, 610100, Sichuan, China

    Wenzheng Chen, Gang He, Guanglei Yang, Jinsong Li & Changhu Xiang

  2. Graduate School of Shandong Sport University, Shandong Sport University, Jinan, 250102, Shandong, China

    Wenzheng Chen

  3. Department of Educational Foundation and Humanities, Faculty of Education, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Syed Kamaruzaman Bin Syed Ali

  4. Department of Mathematics and Science Education, Faculty of Education, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Hutkemri Zulnaidi

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  1. Wenzheng Chen
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Contributions

W.Z. Chen conceived the study, designed the methodology, supervised the research process, and drafted the main manuscript text.S.K.B.S. Ali contributed to theoretical development, cross-cultural validation, and manuscript review.H. Zulnaidi provided methodological guidance, statistical validation, and critical revision of the manuscript.G. He assisted with data curation, software implementation, and preliminary analysis.G. Yang contributed to investigation, visualization, and preparation of supplementary materials.J. Li participated in data collection, resource coordination, and project administration.C. Xiang contributed to validation, editing, and refinement of the final manuscript.All authors reviewed and approved the final version of the manuscript.

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Correspondence to Wenzheng Chen.

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Chen, W., Syed Ali, S.K.B., Zulnaidi, H. et al. An explainable dual-attention transformer for predicting the sociocultural impact of global sports events. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43247-8

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  • Received: 18 November 2025

  • Accepted: 03 March 2026

  • Published: 09 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-43247-8

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Keywords

  • Artificial intelligence
  • Computing methods
  • Convolutional neural networks
  • Cultural impact analysis
  • Data acquisition
  • Expert systems
  • Hypertext analysis
  • Information technology
  • Machine learning
  • Neural networks
  • Sports analytics
  • Text mining
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