Abstract
Climate change remains one of the most urgent challenges of our time, yet traditional methods of engaging the public often fail to effectively mobilize action. This presentation explores a novel approach to climate communication by examining the role of emotions in shaping public perceptions and behaviors toward climate change. Drawing on advancements in natural language processing (NLP), sentiment analysis, and topic modeling, this research proposes to harness social media data as a lens through which emotional responses to greenhouse gas (GHG) emissions can be identified and analyzed. By focusing on emotions such as fear, hope, and anger, this approach aims to uncover how emotional engagement with climate discourse influences pro-environmental behaviors and supports more effective policy development as well as what are the most effective methods to analyze such problem. Although no empirical data has been collected at this stage, the proposed methodology envisions a two-step process: first, extracting and categorizing emotions from social media content, and second, examining how these emotions may correlate with public climate action. Ultimately, this research seeks to contribute to a deeper understanding of the intersection between emotion, technology, and sustainable development, offering guidance for policymakers, environmental communicators, and researchers seeking to foster stronger, emotion-driven engagement with climate change solutions.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2025 Special Focus—Sustainable Development for a Dynamic Planet: Lessons, Priorities, and Solutions
KEYWORDS
EMOTIONS,CLIMATE,COMMUNICATION, SOCIAL MEDIA,NLP,MACHINE LEARNING
