Combat Greenwashing with GoalSpotter: Automatic Sustainability Objective Detection in Heterogeneous Reports. Mahdavi, M., Baghaei Mehr, R., & Debus, T. 2024. Cited by: 0![Combat Greenwashing with GoalSpotter: Automatic Sustainability Objective Detection in Heterogeneous Reports [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Sustainable development is nowadays a prominent factor for the public. As a result, companies publish their sustainability visions and strategies in various reports to show their commitment to saving the environment and promoting social progress. However, not all statements in these sustainability reports are fact-based. When a company tries to mislead the public with its non-fact-based sustainability claims, greenwashing happens. To combat greenwashing, society needs effective automated approaches to identify the sustainability claims of companies in their heterogeneous reports. In this paper, we present a new sustainability objective detection system, named GoalSpotter, that automatically identifies the environmental and social claims of companies in their heterogeneous reports. Our system extracts text blocks of diverse reports, preprocesses and labels them using domain expert annotations, and then fine-tunes transformer models on the labeled text blocks. This way, our system can detect sustainability objectives in any new heterogeneous report. As our experiments show, our system outperforms existing state-of-the-art sustainability objective detection approaches. Furthermore, our post-deployment results show the significant impacts of our system in real-world business. © 2024 ACM.
@CONFERENCE{Mahdavi20244752,
author = {Mahdavi, Mohammad and Baghaei Mehr, Ramin and Debus, Tom},
title = {Combat Greenwashing with GoalSpotter: Automatic Sustainability Objective Detection in Heterogeneous Reports},
year = {2024},
journal = {International Conference on Information and Knowledge Management, Proceedings},
pages = {4752 – 4759},
doi = {10.1145/3627673.3680110},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210037665&doi=10.1145%2f3627673.3680110&partnerID=40&md5=1ee0f2399d6cd550ab0baa1ca37ce513},
affiliations = {Gisma University of Applied Sciences, Potsdam, Germany; Ferris Solutions AG, Cham, Switzerland},
abstract = {Sustainable development is nowadays a prominent factor for the public. As a result, companies publish their sustainability visions and strategies in various reports to show their commitment to saving the environment and promoting social progress. However, not all statements in these sustainability reports are fact-based. When a company tries to mislead the public with its non-fact-based sustainability claims, greenwashing happens. To combat greenwashing, society needs effective automated approaches to identify the sustainability claims of companies in their heterogeneous reports. In this paper, we present a new sustainability objective detection system, named GoalSpotter, that automatically identifies the environmental and social claims of companies in their heterogeneous reports. Our system extracts text blocks of diverse reports, preprocesses and labels them using domain expert annotations, and then fine-tunes transformer models on the labeled text blocks. This way, our system can detect sustainability objectives in any new heterogeneous report. As our experiments show, our system outperforms existing state-of-the-art sustainability objective detection approaches. Furthermore, our post-deployment results show the significant impacts of our system in real-world business. © 2024 ACM.},
author_keywords = {greenwashing; machine learning; natural language processing; sustainability; text mining; transformers},
keywords = {Distribution transformers; Green development; Greenwashing; Language processing; Machine-learning; Natural language processing; Natural languages; Social progress; Sustainability objectives; Sustainability report; Text-mining; Transformer; Sustainable development goals},
publisher = {Association for Computing Machinery},
issn = {21550751},
isbn = {979-840070436-9},
language = {English},
abbrev_source_title = {Int Conf Inf Knowledge Manage},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 0}
}
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