用AI分析上万条推特评论，发现2024大选前移民与民主最受关注。
From Keywords to Clusters: AI-Driven Analysis of YouTube Comments to Reveal Election Issue Salience in 2024
- 通过NLP与聚类分析挖掘八千余条评论，量化议题热度。
- 移民和民主话题提及频次最高，通胀提及显著偏低。
- 基于用户原始评论的分析比传统问卷更真实反映选民关注点。
本文旨在探讨两种竞争性的数据科学方法，以回答‘哪些议题对2024年总统选举选民选择影响最大’这一问题。研究采用人工智能技术，基于自然语言处理与聚类分析，挖掘来自《华尔街日报》（右倾）和《纽约时报》（左倾）在大选前一周发布的选举相关视频下的八千余条用户评论，量化特定议题在评论中的出现频率，从而推断潜在选民最关注的议题。实证结果显示，移民与民主是被提及最频繁且最一致的议题，其次为身份政治，而通货膨胀的提及频率显著较低。该结果部分支持了选举后调查的发现，但反驳了通胀作为关键选举议题的普遍假设。这表明，基于原始网络用户数据的舆论挖掘方法，相较传统民意调查，可能更能揭示选举动向。
原文摘要 · Abstract (English)
This paper aims to explore two competing data science methodologies to attempt answering the question, "Which issues contributed most to voters' choice in the 2024 presidential election?" The methodologies involve novel empirical evidence driven by artificial intelligence (AI) techniques. By using two distinct methods based on natural language processing and clustering analysis to mine over eight thousand user comments on election-related YouTube videos from one right leaning journal, Wall Street Journal, and one left leaning journal, New York Times, during pre-election week, we quantify the frequency of selected issue areas among user comments to infer which issues were most salient to potential voters in the seven days preceding the November 5th election. Empirically, we primarily demonstrate that immigration and democracy were the most frequently and consistently invoked issues in user comments on the analyzed YouTube videos, followed by the issue of identity politics, while inflation was significantly less frequently referenced. These results corroborate certain findings of post-election surveys but also refute the supposed importance of inflation as an election issue. This indicates that variations on opinion mining, with their analysis of raw user data online, can be more revealing than polling and surveys for analyzing election outcomes.
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