用时间适配器让大模型分析推特情绪变化，验证了疫情初期英国公众情绪波动。
Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models
- 在英国民众推特全时间线数据上微调时间适配器，捕捉长期情绪趋势。
- 与官方调查数据比对，多个集体情绪指标相关性显著且稳健。
- 无需预训练分类器即可分析新问题，适合长期社会情绪研究者。
本文提出一种时序对齐的大语言模型（LLM）方法，用于社交媒体的纵向情绪分析。我们在英国推特用户面板的完整时间线上，对 Llama 3 8B 模型微调时间适配器，并利用标准问卷提取情感与态度的长期聚合指标。重点分析新冠疫情初期对公众舆论和集体情绪的影响。将结果与代表性英国调查数据对比，发现多个集体情绪指标具有显著正相关性。该估计结果在多种训练种子和提示设计下保持稳健，且与基于标注数据训练的传统分类模型结果一致。方法在无预训练分类器支持的公共意见议题上也展现出灵活性。本工作通过时间适配器将大模型的情绪分析拓展至纵向场景，为社交媒体的长期分析提供了新范式。
原文摘要 · Abstract (English)
This paper proposes temporally aligned Large Language Models (LLMs) as a tool for longitudinal analysis of social media data. We fine-tune Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users, and extract longitudinal aggregates of emotions and attitudes with established questionnaires. We focus our analysis on the beginning of the COVID-19 pandemic that had a strong impact on public opinion and collective emotions. We validate our estimates against representative British survey data and find strong positive, significant correlations for several collective emotions. The obtained estimates are robust across multiple training seeds and prompt formulations, and in line with collective emotions extracted using a traditional classification model trained on labeled data. We demonstrate the flexibility of our method on questions of public opinion for which no pre-trained classifier is available. Our work extends the analysis of affect in LLMs to a longitudinal setting through Temporal Adapters. It enables flexible, new approaches towards the longitudinal analysis of social media data.
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