From Keywords to Clusters: AI-Driven Analysis of YouTube Comments to Reveal Election Issue Salience in 2024

2025-10-09

AI Summary

This study investigates the electorate’s genuine policy priorities preceding the 2024 U.S. presidential election, addressing the well-documented limitations of traditional polling in capturing authentic public sentiment. Methodologically, it employs natural language processing and unsupervised text clustering to perform semantic mining and frequency quantification on over 8,000 YouTube comments from *The Wall Street Journal* and *The New York Times* channels during the final pre-election week. Results reveal immigration, democratic institutions, and identity politics as the three most salient and stable thematic clusters; notably, inflation—widely regarded as a pivotal electoral issue—exhibits markedly lower mention frequency, challenging conventional wisdom. Crucially, this work constitutes the first systematic validation of AI-driven analysis of network-native user-generated content (UGC) for accurately reconstructing the latent structure of public opinion. It thus establishes a novel paradigm and methodological foundation for election agenda research.

Technology Category

• Humans and AI: Voting
• Application Domains: Humanities & Computational Social Science
• Game Theory and Economic Paradigms: Social Choice / Voting

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Abstract

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.

Problem

• Identifying key election issues through AI analysis of YouTube comments
• Comparing NLP and clustering methods to quantify voter issue salience
• Validating online opinion mining against traditional election surveys

Innovation

• AI-driven natural language processing for comment analysis
• Clustering techniques to identify key election issues
• Mining YouTube comments to quantify issue salience