Generative AI Usage by Individuals During the 2024 U.S. Presidential Election: Symmetrical and Asymmetrical Analysis

With generative artificial intelligence’s (GenAI) growing popularity, individuals are increasingly using it when searching for election-related information. This scenario raises concerns that GenAI usage may result in widespread dissemination of misinformation, given its ability to generate seemingly authentic information. Nevertheless, despite the importance of Gen AI, few researchers have examined how individuals use this tool to search for election-related information. This study aims to assess how GenAI’s perceived system (i.e., accessibility and integration) and information quality (i.e., completeness, accuracy, and neutrality) impact its usage. Focusing on the 2024 U.S. presidential election, we conducted a two-wave survey and data was collected from 364 Americans. Participants were found to have a favorable attitude overall toward GenAI. Further, accuracy and neutrality were positively associated with GenAI usage. A fuzzy set qualitative comparative analysis was also conducted to identify different configurations of perceived system and information quality that led to high GenAI usage. Analyzing the qualitative responses further confirmed the results. This study contributes to the literature on the role of GenAI during elections, providing a nuanced understanding of how dimensions of GenAI’s perceived system and information quality impact individuals’ GenAI usage. The findings have significant practical implications for dealing with the (mis)information generated by GenAI.

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Publication Details

Journal
Social Science Computer Review
Published
2025-10-28
DOI
https://doi.org/10.1177/08944393251392913
Primary Topic
Misinformation and Its Impacts
Type
article
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article

Generative AI Usage by Individuals During the 2024 U.S. Presidential Election: Symmetrical and Asymmetrical Analysis

Xuequn Wang, Yibai Li, Wanli Liu
Social Science Computer Review
Misinformation and Its Impacts
article

Generative AI Usage by Individuals During the 2024 U.S. Presidential Election: Symmetrical and Asymmetrical Analysis

Xuequn Wang, Yibai Li, Wanli Liu
article en

Abstract

With generative artificial intelligence’s (GenAI) growing popularity, individuals are increasingly using it when searching for election-related information. This scenario raises concerns that GenAI usage may result in widespread dissemination of misinformation, given its ability to generate seemingly authentic information. Nevertheless, despite the importance of Gen AI, few researchers have examined how individuals use this tool to search for election-related information. This study aims to assess how GenAI’s perceived system (i.e., accessibility and integration) and information quality (i.e., completeness, accuracy, and neutrality) impact its usage. Focusing on the 2024 U.S. presidential election, we conducted a two-wave survey and data was collected from 364 Americans. Participants were found to have a favorable attitude overall toward GenAI. Further, accuracy and neutrality were positively associated with GenAI usage. A fuzzy set qualitative comparative analysis was also conducted to identify different configurations of perceived system and information quality that led to high GenAI usage. Analyzing the qualitative responses further confirmed the results. This study contributes to the literature on the role of GenAI during elections, providing a nuanced understanding of how dimensions of GenAI’s perceived system and information quality impact individuals’ GenAI usage. The findings have significant practical implications for dealing with the (mis)information generated by GenAI.

Social Science Computer Review
University of Scranton (US), Sun Yat-sen University (CN), University of New Mexico (US), Anhui Xinhua University (CN), Guangzhou Xinhua University
Openalex Percentile: Top 59%
Misinformation and Its Impacts
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Generative AI Usage by Individuals During the 2024 U.S. Presidential Election: Symmetrical and Asymmetrical Analysis — Xuequn Wang, Yibai Li, et al. · Social Science Computer Review (2025) | TGRS Research Map | TGRS