用大模型自动分析社交媒体中的出行方式与情绪，省去人工标注
From Twitter to Reasoner: Understand Mobility Travel Modes and Sentiment Using Large Language Models
- 用大模型从社交媒体文本中识别出行方式和态度，无需人工标注
- 发现多数帖子情绪为负面，主要因服务延误、拥挤等问题引发
- 适合交通运营方与政策制定者参考，优化出行服务体验
社交媒体已成为公众表达对交通服务与基础设施意见的重要平台，为研究者深入理解出行选择、运营商提升服务质量、政策制定者规范出行管理提供了潜在数据支持。然而，社交媒体数据具有高度非结构化特征，文本未标注，大规模人工标注成本过高。本研究提出一种基于大语言模型（LLMs）的新方法框架，无需人工标注即可从社交媒体帖子中推断提及的出行方式，并推理人们对相关出行方式的态度。我们通过人类评估与大模型验证，对比了不同大模型及提示工程方法的表现。结果表明，多数社交媒体帖子呈现负面情绪而非正面情绪。据此，我们识别出负面情绪的主要驱动因素，并向交通运营商和政策制定者提出针对性改进建议。
原文摘要 · Abstract (English)
Social media has become an important platform for people to express their opinions towards transportation services and infrastructure, which holds the potential for researchers to gain a deeper understanding of individuals' travel choices, for transportation operators to improve service quality, and for policymakers to regulate mobility services. A significant challenge, however, lies in the unstructured nature of social media data. In other words, textual data like social media is not labeled, and large-scale manual annotations are cost-prohibitive. In this study, we introduce a novel methodological framework utilizing Large Language Models (LLMs) to infer the mentioned travel modes from social media posts, and reason people's attitudes toward the associated travel mode, without the need for manual annotation. We compare different LLMs along with various prompting engineering methods in light of human assessment and LLM verification. We find that most social media posts manifest negative rather than positive sentiments. We thus identify the contributing factors to these negative posts and, accordingly, propose recommendations to traffic operators and policymakers.
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