Climate Change Pulse: A RAG-Driven Interactive Platform for Exploring Disaster-Linked Climate Sentiment on Social Media

Abstract: Public engagement with climate change tends to peak during extreme-weather events and dissipate soon thereafter, yet the quantitative relationships among spatial proximity, temporal context, and ideological stance remain under-explored. We present Climate Change Pulse (https://climatechangepulse.org), an open-access web platform that unifies a geovisual dashboard of 15 million climate-related tweets with 9094 EM-DAT disaster records and augments the interface with an agentic Retrieval-Augmented Generation (RAG) chatbot. The system allows users to pose natural-language questions (e.g., “What was climate-change sentiment around the 2021 German floods?”) and receive answers drawn dynamically from two in-memory SQLite databases.Research Questions: RQ1. How does spatial proximity to a disaster correlate with the polarity of climate-related tweets?RQ2. How do sentiment trends differ across stance categories (believer, neutral, denier) in the days surrounding an event?RQ3. Can an agentic RAG loop provide reliable conversational access to large, tabular climate datasets?System Architecture:A large-language model (LLM) receives database schemas plus the user prompt, generates SQL queries, and validates them through a three-retry error-feedback loop. A timeline widget enables year-by-year navigation, while selecting a disaster overlays the associated tweet cluster and surfaces embedded tweets for qualitative inspection.Experimental Design:Tweets were analysed in three concentric distance bands (≤ 500 km, 500–1 000 km, ≥ 1 000 km) and three temporal windows (1, 3, and 7 days before/after impact) for 730 climate-relevant disasters between 2006 and 2019. Sentiment and stance labels were taken from the publicly released Climate-Change-Twitter Dataset. Qualitative heat-maps and mean-sentiment plots were generated to visualise proximity and stance effects. The RAG component was exercised with a battery of representative user queries to confirm syntactic correctness and conversational fallback behaviour.Findings:Tweets originating in the ≤ 500 km band consistently expressed more negative sentiment than those posted at greater distances, supporting the intuition that proximity amplifies emotional response.Across all temporal windows, denier tweets were markedly more negative than believer or neutral tweets; the neutral category displayed the most stable sentiment.The agentic RAG loop successfully returned validated SQL and, when faced with an unanswerable request, provided an explanatory fallback, illustrating its suitability for interactive exploration.Contributions:Interactive AI for social computing: First integration of geovisual disaster analytics with a RAG chatbot, enabling conversational interrogation of multimillion-record climate corpora.Empirical insight: Qualitative evidence linking disaster proximity, ideological stance, and sentiment dynamics across thirteen years of Twitter data.Open science: Source code, processed datasets, and prompt templates released under an MIT licence to facilitate replication and extension (https://github.com/CCOh125/climatechangepulse.github.io).Future WorkOngoing efforts include real-time ingestion of live disaster feeds, fine-tuning domain-specific language models for richer affective nuance, and controlled user studies to assess decision-support value in crisis-communication contexts.

Keywords: Climate sentiment, agentic RAG, interactive visualization, geospatial social media analytics, disaster communication, AI for social good

DOI: 10.54941/ahfe1006274

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