用大模型分析微博疫情情绪，发现政府行动影响公众心态变化
Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers
- 用Llama 3 8B模型将微博情绪分为正向、负向、讽刺、中性四类
- 疫情期情绪从初期中性转向后期负面，政府防控措施发布后情绪明显改善
- 揭示中文社交平台在重大危机中的情绪演变规律，适合舆情研究者参考
研究危机期间公众情绪对理解观点演变和极化社会至关重要。本文以中国最受欢迎的微博平台为对象，分析新冠疫情爆发前、爆发期及防控初期的用户帖子。利用大语言模型Llama 3 8B，将用户情绪分类为正面、负面、讽刺与中性四类。通过分析微博情绪变化，揭示社会事件与政府行动如何影响公众舆论。本研究填补了中文社交平台在健康危机中情感分析的空白，有助于理解数字传播在应对全球性挑战时对社会反应的塑造作用。
原文摘要 · Abstract (English)
Studying public sentiment during crises is crucial for understanding how opinions and sentiments shift, resulting in polarized societies. We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis. The study period includes the pre-COVID-19 stage, the outbreak stage, and the early stage of epidemic prevention. We use Llama 3 8B, a Large Language Model, to analyze users' sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories. Analyzing sentiment shifts on Weibo provides insights into how social events and government actions influence public opinion. This study contributes to understanding the dynamics of social sentiments during health crises, fulfilling a gap in sentiment analysis for Chinese platforms. By examining these dynamics, we aim to offer valuable perspectives on digital communication's role in shaping society's responses during unprecedented global challenges.
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