Abstract
The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On the other hand, traditional unsupervised methods for extracting themes from public discourse, such as topic modeling, often reveal overarching patterns that might not capture specific nuances. Consequently, a significant portion of research into social media discourse still depends on labor-intensive manual coding techniques and a human-in-the-loop approach, which are both time-consuming and costly. In this work, we study the problem of discovering arguments associated with a specific theme. We propose a generic LLMs-in-the-Loop strategy that leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging. To demonstrate our approach, we apply our framework to contentious topics. We use two publicly available datasets: (1) the climate campaigns dataset of 14k Facebook ads with 25 themes and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads with 14 themes. Furthermore, we analyze demographic targeting and the adaptation of messaging based on real-world events.
Abstract (translated)
社交媒体的广泛应用导致了对自动分析公共舆论的自动化方法的浓厚兴趣。监督方法擅长文本分类，但由于社交媒体讨论的动态性，这些技术因持续关注焦点转移而面临持续的挑战。另一方面，从公共话语中提取主题的传统无监督方法，如主题建模，通常揭示出可能不会捕捉到具体细微之处的总体模式。因此，研究社交媒体讨论的大部分仍然依赖于劳动密集型的手动编码技术和人机交互方法，这些方法既耗时又昂贵。在这项工作中，我们研究了发现与特定主题相关的论点的问题。我们提出了一种通用的LLMs-in-the-Loop策略，利用大型语言模型的先进功能来从社交媒体消息中提取潜在论点。为了证明我们的方法，我们将其应用于有争议的话题。我们使用了两个公开可用的数据集：（1）14k个Facebook广告的气候变化活动数据集，分为25个主题；（2）9k个Facebook广告的新冠疫苗活动数据集，分为14个主题。此外，我们还分析了基于现实事件的人口统计学分析和消息适应。
URL
https://arxiv.org/abs/2404.10259
