Authors:
Yue Zhao
1
;
Leon Kroher
2
;
Maximilian Engler
2
and
Klemens Schnattinger
2
Affiliations:
1
Intergration Alpha GmbH, Fabrikstrasse 5, 6330 Cham, Switzerland
;
2
Business Innovation Center, Baden-Wuerttemberg Cooperative State University (DHBW), Hangstraße 46-50, 79539 Loerrach, Germany
Keyword(s):
Greenwashing, Natural Language Processing (NLP), Environmental, Social, and Governance (ESG), Sentiment Analysis, Question-and-Answer Generation, Pharmaceutical Firms, Public Perception, Social Media, Monitoring Mechanisms.
Abstract:
Greenwashing, where companies misleadingly project environmental, social, and governance (ESG) virtues, challenges stakeholders. This study examined the link between internal ESG sentiments and public opinion on social media across 12 pharmaceutical firms from 2012 to 2022. Using natural language processing (NLP), we analyzed internal documents and social media. Our findings showed no significant correlation between internal and external sentiment scores, suggesting potential greenwashing if there’s inconsistency in sentiment. This inconsistency can be a red flag for stakeholders like investors and regulators. In response, we propose an NLP-based Q&A system that generates context-specific questions about a company’s ESG performance, offering a potential solution to detect greenwashing. Future research should extend to other industries and additional data sources like financial disclosures.