This paper introduces a novel approach to uncovering and analyzing themes in social media messaging. Recognizing the limitations of traditional topic-level analysis, which tends to capture only the overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Conventional methods of theme discovery, involving manual processes and a human-in-the-loop approach, are valuable but face challenges in scalability, consistency, and resource intensity in terms of time and cost. To address these challenges, we propose a machine-in-the-loop approach that leverages the advanced capabilities of Large Language Models (LLMs). This approach allows for a deeper investigation into the thematic aspects of social media discourse, enabling us to uncover a diverse array of themes, each with unique characteristics and relevance, thereby offering a comprehensive understanding of the nuances present within broader topics. Furthermore, this method efficiently maps the text and the newly discovered themes, enhancing our understanding of the thematic nuances in social media messaging. We employ climate campaigns as a case study and demonstrate that our methodology yields more accurate and interpretable results compared to traditional topic models. Our results not only demonstrate the effectiveness of our approach in uncovering latent themes but also illuminate how these themes are tailored for demographic targeting in social media contexts. Additionally, our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.

翻译：本文提出了一种新颖的方法来揭示和分析社交媒体信息中的主题。鉴于传统主题级分析仅捕捉总体模式这一局限性，本研究强调需要更细粒度、聚焦主题的探索。传统主题发现方法涉及人工流程和人机协同，虽具价值，但在可扩展性、一致性和时间成本方面面临挑战。为应对这些挑战，我们提出了一种机器参与的流程，利用大语言模型（LLMs）的先进能力。该方法能够深入探究社交媒体话语的主题层面，揭示具有独特特征和相关性的一系列主题，从而全面理解更广泛主题中的细微差别。此外，该方法能高效地将文本与新发现主题进行映射，增强我们对社交媒体信息中主题细微差别的理解。我们以气候运动为案例，证明本方法相比传统主题模型能产生更准确且可解释的结果。研究结果不仅展示了该方法在揭示潜在主题方面的有效性，更阐明了这些主题如何在社交媒体语境中针对人口特征进行定向设计。此外，我们的工作揭示了社交媒体的动态本质，展现了信息主题焦点如何随现实世界事件变化而迁移。