Fake news detection for sustainable supply chain management using deceptive tones: an application of ML, NLP and Explainable AI

被引：0

作者：

Pant, Pushpesh ^{[1
]}

Yadav, Rohit ^{[2
]}

Niyas, K. P. Muhammed ^{[3
]}

Dadsena, Krishna Kumar ^{[2
]}

Pratap, Saurabh ^{[4
,5
]}

Sachan, Amit ^{[2
]}

机构：

[1] Inst Management Technol, Operat Dept, Hyderabad Campus, Hyderabad, India

[2] Indian Inst Management Ranchi, Operat Dept, Ranchi, India

[3] Rajagiri Business Sch, Informat Syst & Business Analyt, Kochi, India

[4] Indian Inst Technol BHU, Dept Mech Engn, Varanasi, India

[5] Indian Inst Technol BHU, Sch Decis Sci & Engn, Varanasi, India

来源：

ENTERPRISE INFORMATION SYSTEMS | 2025年

关键词：

Fake news; deceptive tones; sustainable supply chain; explainable AI; NLP; linguistic features; PERFORMANCE; SENTIMENT;

D O I：

10.1080/17517575.2025.2510350

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study develops a novel framework to detect deceptive tones related to sustainable supply chain (SSC) in annual disclosure. Using natural language processing, machine learning, and explainable AI, the model extracts 15 linguistic features from annual reports to compute a deceptive score related to SSC. The findings reveal a positive relationship between deceptive scores and increased word count, and therefore, suggest strategic manipulation in SSC narratives. In essence, the present paper develops a robust tool that can be integrated into ERP/CRM systems to enable automated detection of SSC-related misleading claims, making it highly valuable for investors, regulators, and enterprises.

引用

页数：31

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