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Machine learning based attribution mapping of climate related discussions on social media
A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new public address system, makes it an ideal source of information to assess public discussions and respons...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643343/ https://www.ncbi.nlm.nih.gov/pubmed/36347895 http://dx.doi.org/10.1038/s41598-022-22034-1 |
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author | Kaushal, Akshay Acharjee, Animesh Mandal, Anandadeep |
author_facet | Kaushal, Akshay Acharjee, Animesh Mandal, Anandadeep |
author_sort | Kaushal, Akshay |
collection | PubMed |
description | A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new public address system, makes it an ideal source of information to assess public discussions and responses in real time. We mine c.1.7 m posts from 55 climate related subreddits on social media platform Reddit since its inception. Using USE, a state-of-the-art sentence encoder, and K-means clustering algorithm, we develop a machine learning based approach to identify, store, process and classify the posts automatically, and at a scale. In the broad and multifaceted theme of climate change, our approach narrows down the focus to 10 critical underlying themes comprising the public discussions on social media over time. Furthermore, we employ a full order partial correlation analysis to assess the relationship between the different identified themes. We show that in line with Paris Agreement, while the climate science community has been successful in influencing the discussions on both the causes and effects of climate change, the public administration has failed to appropriately communicate the causes of climate change and has been able to influence only the discussions on the effects of it. Hence, our study shows a clear gap in the public communication by the administration, wherein counter-intuitively less emphasis has been given on the drivers of climate change. This information can be particularly beneficial to policymakers and climate activists in decision making as they try to close the gap between public and academia. |
format | Online Article Text |
id | pubmed-9643343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96433432022-11-14 Machine learning based attribution mapping of climate related discussions on social media Kaushal, Akshay Acharjee, Animesh Mandal, Anandadeep Sci Rep Article A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new public address system, makes it an ideal source of information to assess public discussions and responses in real time. We mine c.1.7 m posts from 55 climate related subreddits on social media platform Reddit since its inception. Using USE, a state-of-the-art sentence encoder, and K-means clustering algorithm, we develop a machine learning based approach to identify, store, process and classify the posts automatically, and at a scale. In the broad and multifaceted theme of climate change, our approach narrows down the focus to 10 critical underlying themes comprising the public discussions on social media over time. Furthermore, we employ a full order partial correlation analysis to assess the relationship between the different identified themes. We show that in line with Paris Agreement, while the climate science community has been successful in influencing the discussions on both the causes and effects of climate change, the public administration has failed to appropriately communicate the causes of climate change and has been able to influence only the discussions on the effects of it. Hence, our study shows a clear gap in the public communication by the administration, wherein counter-intuitively less emphasis has been given on the drivers of climate change. This information can be particularly beneficial to policymakers and climate activists in decision making as they try to close the gap between public and academia. Nature Publishing Group UK 2022-11-08 /pmc/articles/PMC9643343/ /pubmed/36347895 http://dx.doi.org/10.1038/s41598-022-22034-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kaushal, Akshay Acharjee, Animesh Mandal, Anandadeep Machine learning based attribution mapping of climate related discussions on social media |
title | Machine learning based attribution mapping of climate related discussions on social media |
title_full | Machine learning based attribution mapping of climate related discussions on social media |
title_fullStr | Machine learning based attribution mapping of climate related discussions on social media |
title_full_unstemmed | Machine learning based attribution mapping of climate related discussions on social media |
title_short | Machine learning based attribution mapping of climate related discussions on social media |
title_sort | machine learning based attribution mapping of climate related discussions on social media |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643343/ https://www.ncbi.nlm.nih.gov/pubmed/36347895 http://dx.doi.org/10.1038/s41598-022-22034-1 |
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