Exploring the climate change discourse on Chinese social media and the role of social bots

ABSTRACTWhile climate change discourse on Western platforms like Twitter often reveals signs of polarization and misinformation, discussions on Chinese social media remain less explored. Building on the theoretical framework of the green public sphere, this study aims to explore the features of the content (topics and veracity), the characteristics of engaged users (regular users and social bots), and the communication strategies adopted by engaged users in climate change discussions on Chinese social media. We employed machine learning methods to analyze 452,167 climate change-related posts generated by 311,214 users from 2010 to 2020 on Weibo, finding that climate change discourse concentrated on environmental and health impacts and action advocacy, and misinformation was not prevalent. Regarding the composition of engaged users, only a small proportion were social bots which concentrated on action advocacy and politics and governance, rather than skeptical and denialist discourses. In terms of communication strategies, we found that social bots on Weibo were more likely to forward a post or mention another user than regular users. This study expands our understanding of climate change discourse and the green public sphere on social media and provides insights into leveraging social bots in climate change communication in an AI-powered society.KEYWORDS: Climate changepublic discoursetopicsmisinformationsocial botssocial mediagreen public sphere Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by National Key Research and Development Program of China [Grant Number 2021YFF0901601].Notes on contributorsJiaojiao JiJiaojiao Ji (Ph.D., University of Science and Technology of China) is currently a Senior Research Fellow in the Department of Communication of Science and Technology at the University of Science and Technology of China. She was a visiting scholar at the University of California, Davis (2016–2017) and a visiting scholar at Annenberg School of Communication and Journalism, University of Southern California (2018–2019). Her interests lie in public opinion on social media, misinformation detection and correction, and computational methods.Ting HuTing Hu is currently a Ph.D. student in the Department of Communication of Science and Technology at the University of Science and Technology of China (USTC). Her current research interests include science communication and health communication on social media, computational methods in communication. Her research has been accepted by the 17th Public Communication of Science and Technology (PCST2023) conference.Zihang ChenZihang Chen is a master student majoring in Software Engineering in the Institute of Advanced Technology at the University of Science and Technology of China (USTC) in Hefei, China. He earned his Bachelor of Science degree in Computer Science and Technology from Jimei University in Xiamen, China, in 2020. His research focuses on data mining, natural language processing, and machine learning, and he has published a conference paper in these related fields.Mengxiao ZhuMengxiao Zhu is a Distinguished Research Fellow in the School of Humanities and Social Sciences, at the University of Science and Technology of China (USTC). She earned her Ph.D. Degree in Industrial Engineering and Management Sciences from Northwestern University. Before joining USTC, she worked as a Research Scientist in the Research and Development division at Educational Testing Service (ETS) for over seven years. Her current research interests include computational methods in communication, social networks and social media, and the interactions of AI and human in communication and education.