Tweeting Circular Economy: Unveiling Current Discourse Through Natural Language Processing

ABSTRACT In recent years, the circular economy (CE) has emerged as a critical strategy for sustainable development, addressing environmental challenges, resource efficiency, and waste reduction. Although the advantages of CE over traditional linear economic models are well‐documented, less attention has been given to understanding how CE is perceived and discussed in public forums such as social media. This paper aims to fill that gap by analyzing Twitter conversations related to CE from 2012 to 2022, using advanced Natural Language Processing (NLP) techniques. A total of 389,575 tweets were collected and processed to uncover the main themes, trends, and influential entities in the global CE discourse. The analysis shows that waste management, recycling, and resource efficiency are the most prevalent topics, reflecting the public's concern for practical CE applications. Additionally, technological advancements such as blockchain and IoT are emerging as significant drivers of CE innovation. Countries like the United Kingdom, China, and India, along with key organizations like the European Commission and the Ellen MacArthur Foundation, are central to the global CE narrative. These findings provide critical insights for policymakers, corporations, and researchers, highlighting areas of public interest and potential collaboration to advance CE initiatives. By aligning strategies with social media discourse, stakeholders can promote more effective, widespread adoption of CE principles.

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Publication Details

Journal
Sustainable Development
Published
2024-12-27
DOI
https://doi.org/10.1002/sd.3323
Citations
1
Primary Topic
Sustainable Supply Chain Management
Type
article
Field-Weighted Citation Impact
0.55
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article

Tweeting Circular Economy: Unveiling Current Discourse Through Natural Language Processing

Muhammad Syafrudin, Norma Latif Fitriyani, Muhammad Rifqi Maarif, Arif Rahman Saleh et al.
1 citations
Sustainable Development
Sustainable Supply Chain Management
0.55
article

Tweeting Circular Economy: Unveiling Current Discourse Through Natural Language Processing

Muhammad Syafrudin, Norma Latif Fitriyani, Muhammad Rifqi Maarif, Arif Rahman Saleh, Arif Budy Pratama, Michael Hutahaean
article en
1 citations

Abstract

ABSTRACT In recent years, the circular economy (CE) has emerged as a critical strategy for sustainable development, addressing environmental challenges, resource efficiency, and waste reduction. Although the advantages of CE over traditional linear economic models are well‐documented, less attention has been given to understanding how CE is perceived and discussed in public forums such as social media. This paper aims to fill that gap by analyzing Twitter conversations related to CE from 2012 to 2022, using advanced Natural Language Processing (NLP) techniques. A total of 389,575 tweets were collected and processed to uncover the main themes, trends, and influential entities in the global CE discourse. The analysis shows that waste management, recycling, and resource efficiency are the most prevalent topics, reflecting the public's concern for practical CE applications. Additionally, technological advancements such as blockchain and IoT are emerging as significant drivers of CE innovation. Countries like the United Kingdom, China, and India, along with key organizations like the European Commission and the Ellen MacArthur Foundation, are central to the global CE narrative. These findings provide critical insights for policymakers, corporations, and researchers, highlighting areas of public interest and potential collaboration to advance CE initiatives. By aligning strategies with social media discourse, stakeholders can promote more effective, widespread adoption of CE principles.

Sustainable DevelopmentVol. 33(3)
Sejong University (KR), Universitas Tidar (ID)
Decent work and economic growth
Openalex Percentile: Top 19%
Sustainable Supply Chain Management
0.55
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Tweeting Circular Economy: Unveiling Current Discourse Through Natural Language Processing — Muhammad Syafrudin, Norma Latif Fitriyani, et al. · Sustainable Development (2024) | TGRS Research Map | TGRS