SPECIAL SESSION 1: Big Data Analytics for Misinformation and Sustainability Governance

With the rapid expansion of digital platforms and the increasing emphasis on sustainability and ESG governance, industrial systems are facing growing challenges related to misinformation, counterfeit products, greenwashing, and unreliable carbon emission disclosure. These issues often arise from complex interactions between structured numerical indicators and unstructured textual information, such as online reviews, product descriptions, corporate news, and ESG reports.

Recent advances in big data analytics including machine learning, deep learning and multimodal data integration provide new opportunities to address these challenges. In particular, explainable artificial intelligence (XAI) has emerged as a critical approach for enhancing model transparency, interpretability, and trustworthiness in high-stakes decision-making contexts.

This special session aims to provide a focused forum for presenting theoretical advances, methodological innovations, and applied studies on big data analytics, explainable and multimodal AI techniques for ESG risk analysis, misinformation detection, and sustainability-oriented decision support. Contributions that demonstrate practical relevance to industrial applications, regulatory assessment, and supply chain governance are especially encouraged.

Topics covered include (but are not limited to):

  • Big data analytics for industrial and sustainability
  • Explainable AI (XAI) methods for industrial and sustainability applications
  • Multimodal learning integrating numerical, textual, and behavioral data
  • Greenwashing detection and ESG disclosure analytics
  • Corporate carbon emission modeling and prediction
  • Misinformation and counterfeit detection in e-commerce platforms
  • Text mining and NLP for ESG reports, corporate news, and online reviews
  • AI-driven decision support for sustainability governance and compliance
  • Supply chain risk analytics and integrity management
  • Interpretable machine learning for policy, regulatory, and managerial use
     

chairman:

Prof. Mu-Chen Chen, National Yang Ming Chiao Tung University

Biodata: Mu-Chen Chen received his Ph.D. and M.Sc. degrees in Industrial Engineering and Management from National Chiao-Tung University, and his B.S. degree in Industrial Engineering from Chung Yuan Christian University. He is currently a Professor in the Department of Transportation and Logistics Management at National Yang Ming Chiao Tung University. His research interests include big data analytics, logistics and supply chain management, and meta-heuristics, with a particular focus on data-driven decision support and intelligent analytics for complex industrial and sustainabilityrelated problems.
 

Prof. Long-Sheng Chen, National Taipei University of Technology

Biodata: Long-Sheng Chen is a professor at the Department of Industrial Engineering and Management, National Taipei University of Technology, and serves as Deputy Secretary General of the Chinese Institute of Industrial Engineers. He holds a Ph.D. in Industrial Engineering and Management from National Chiao Tung University, along with M.S. and B.S. degrees in Industrial and Information Management from National Cheng Kung University. His research focuses on big data analytics, social media marketing, Six Sigma, innovation in service and product development, quality engineering and management, and granular computing.

SUBMISSION

Delegates are encouraged to submit their full papers/abstract to the special sessions. Please submit your electronically article in PDF format before the submission deadline.

Submit Now: http://www.easychair.org/conferences/?conf=iciea2026
Please select the special session 1 when making submission.

Note: if there are any questions, please send mail to iciea_conf@163.com.

Menu