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.

该研究介绍了一种用于揭示和分析社交媒体信息主题的新方法，通过机器与人员合作，利用大型语言模型，旨在深入探索社交媒体信息的主题细节，并发掘出多样的主题，研究结果表明该方法相较于传统的主题模型在发现潜在主题上更准确和可解释，同时揭示社交媒体中主题焦点随着现实事件的变化而变动，并指出这些主题如何针对不同人群进行定制。

通过整合 LLMs 揭示社交媒体上的潜在主题：以气候运动为例的案例研究

Uncovering Latent Themes of Messaging on Social Media by Integrating  LLMs: A Case Study on Climate Campaigns

Experts across diverse disciplines are often interested in making sense of
large text collections. Traditionally, this challenge is approached either by
noisy unsupervised techniques such as topic models, or by following a manual
theme discovery process. In this paper, we expand the definition of a theme to
account for more than just a word distribution, and include generalized
concepts deemed relevant by domain experts. Then, we propose an interactive
framework that receives and encodes expert feedback at different levels of
abstraction. Our framework strikes a balance between automation and manual
coding, allowing experts to maintain control of their study while reducing the
manual effort required.

本研究提出了一种交互式框架，该框架通过接收并编码领域相关的概念和专家反馈来维护领域专家对学习的控制，从而在自动化和手动编码之间取得平衡。