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CLIMATE-RELATED DISCUSSIONS ON SOCIAL MEDIA: CRITICAL LESSONS FOR POLICYMAKERS

Published online by Cambridge University Press:  04 June 2024

Anandadeep Mandal*
Affiliation:
Department of Finance, Birmingham Business School, University of Birmingham, Birmingham, UK
Akshay Kaushal
Affiliation:
HSBC Global Research, HSBC Global Banking and Markets, Bangalore, India
Animesh Acharjee
Affiliation:
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
*
Corresponding author: Anandadeep Mandal; Email: a.mandal@bham.ac.uk
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Abstract

Climate change is a complex global issue that requires widespread understanding, support and collaboration for effective solutions. This research delves into the crucial role of communication in tackling climate change and reaching net-zero goals. Leveraging advanced machine learning techniques, we focus on 10 core climate change topics derived from social media conversations over time. This analysis underscores the importance of a holistic and interconnected approach, involving a diverse array of policies at local, national and global levels to combat climate change effectively and attain net-zero objectives. We offer key policy suggestions that can significantly contribute to this vital cause.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of National Institute of Economic and Social Research
Figure 0

Figure 1. Optimised clusters. The left-hand side shows the scatter plots. The right-hand side shows the size distribution column charts for the same optimised for the same sample.Source: Kaushal et al. (2022)

Figure 1

Figure 2. Proportion of monthly posts in optimised clusters (Jan 2008–Jun 2021). The figure shows the distribution of proportion of monthly posts for the optimised clusters over the entire study period. The solid horizontal line depicts the overall mean of the sample, and the dotted lines depict the ±1 sample standard deviations. Interestingly, we observe that the time-series break out of the one standard deviation above and below mean at different times in different clusters mainly signifying the unusually high or low activity of users during that time due to some of the key climate-related events as highlighted with solid arrows and encircled numbers in various sub-plots.Source: Kaushal et al. (2022)