Your browser doesn't support javascript.
loading
Attention, sentiments and emotions towards emerging climate technologies on Twitter.
Müller-Hansen, Finn; Repke, Tim; Baum, Chad M; Brutschin, Elina; Callaghan, Max W; Debnath, Ramit; Lamb, William F; Low, Sean; Lück, Sarah; Roberts, Cameron; Sovacool, Benjamin K; Minx, Jan C.
Afiliação
  • Müller-Hansen F; Mercator Research Institute on Global Commons and Climate Change (MCC), Germany.
  • Repke T; Mercator Research Institute on Global Commons and Climate Change (MCC), Germany.
  • Baum CM; Department of Business Technology and Development, Aarhus University, Denmark.
  • Brutschin E; International Institute for Applied Systems Analysis (IIASA), Austria.
  • Callaghan MW; Mercator Research Institute on Global Commons and Climate Change (MCC), Germany.
  • Debnath R; Cambridge Collective Intelligence & Design Group, Cambridge Zero and Computer Laboratory, University of Cambridge, United Kingdom.
  • Lamb WF; Division of Humanities and Social Science, California Institute of Technology (Caltech), USA.
  • Low S; Mercator Research Institute on Global Commons and Climate Change (MCC), Germany.
  • Lück S; Department of Business Technology and Development, Aarhus University, Denmark.
  • Roberts C; Mercator Research Institute on Global Commons and Climate Change (MCC), Germany.
  • Sovacool BK; Centre for Sustainability and the Global Environment (SAGE), University of Wisconsin Madison, USA.
  • Minx JC; Department of Business Technology and Development, Aarhus University, Denmark.
Glob Environ Change ; 83: 102765, 2023 Dec.
Article em En | MEDLINE | ID: mdl-38130391
ABSTRACT
Public perception of emerging climate technologies, such as greenhouse gas removal (GGR) and solar radiation management (SRM), will strongly influence their future development and deployment. Studying perceptions of these technologies with traditional survey methods is challenging, because they are largely unknown to the public. Social media data provides a complementary line of evidence by allowing for retrospective analysis of how individuals share their unsolicited opinions. Our large-scale, comparative study of 1.5 million tweets covers 16 GGR and SRM technologies and uses state-of-the-art deep learning models to show how attention, and expressions of sentiment and emotion developed between 2006 and 2021. We find that in recent years, attention has shifted from general geoengineering themes to specific GGR methods. On the other hand, there is little attention to specific SRM technologies and they often coincide with conspiracy narratives. Sentiments and emotions in GGR tweets tend to be more positive, particularly for methods perceived to be natural, but are more negative when framed in the geoengineering context.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article