Climate discourse online plays a crucial role in shaping public understanding of climate change and influencing political and policy outcomes. However, climate communication unfolds across structurally distinct platforms with fundamentally different incentive structures: paid advertising ecosystems incentivize targeted, strategic persuasion, while public social media platforms host largely organic, user-driven discourse. Existing computational studies typically analyze these environments in isolation, limiting our ability to distinguish institutional messaging from public expression. In this work, we present a comparative analysis of climate discourse across paid advertisements on Meta (previously known as Facebook) and public posts on Bluesky from July 2024 to September 2025. We introduce an interpretable, end-to-end thematic discovery and assignment framework that clusters texts by semantic similarity and leverages large language models (LLMs) to generate concise, human-interpretable theme labels. We evaluate the quality of the induced themes against traditional topic modeling baselines using both human judgments and an LLM-based evaluator, and further validate their semantic coherence through downstream stance prediction and theme-guided retrieval tasks. Applying the resulting themes, we characterize systematic differences between paid climate messaging and public climate discourse and examine how thematic prevalence shifts around major political events. Our findings show that platform-level incentives are reflected in the thematic structure, stance alignment, and temporal responsiveness of climate narratives. While our empirical analysis focuses on climate communication, the proposed framework is designed to support comparative narrative analysis across heterogeneous communication environments.

翻译：在线气候话语在塑造公众对气候变化的理解以及影响政治与政策结果方面发挥着至关重要的作用。然而，气候传播在结构迥异、激励模式根本不同的平台上展开：付费广告生态系统激励有针对性的、策略性的说服，而公共社交媒体平台则主要承载有机的、用户驱动的话语。现有的计算研究通常孤立地分析这些环境，限制了我们将机构信息与公众表达区分开来的能力。在本研究中，我们对2024年7月至2025年9月期间Meta（前身为Facebook）上的付费广告与Bluesky上的公共帖子中的气候话语进行了比较分析。我们引入了一个可解释的端到端主题发现与分配框架，该框架通过语义相似性对文本进行聚类，并利用大语言模型生成简洁、人类可理解的主题标签。我们结合人工判断和基于LLM的评估器，将所归纳主题的质量与传统主题建模基线进行比较评估，并通过下游立场预测和主题引导检索任务进一步验证其语义连贯性。应用所得主题，我们描述了付费气候信息与公共气候话语之间的系统性差异，并考察了主要政治事件前后主题流行度的变化。我们的研究结果表明，平台层面的激励反映在气候叙事的主题结构、立场一致性和时间响应性上。虽然我们的实证分析聚焦于气候传播，但所提出的框架旨在支持跨异构传播环境的比较叙事分析。