Signal, Noise, and Burnout: A Human-Information Interaction Analysis of Voter Verification in a High-Volatility Environment
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915692890030080 |
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| author | Lee, Kijung |
| author_facet | Lee, Kijung |
| contents | The 2024 U.S. Presidential Election unfolded within an information environment of unprecedented volatility, challenging citizens to navigate a torrent of rapidly evolving, often contradictory information while determining what to believe. This study investigates the cognitive mechanisms underlying epistemic self-efficacy - the perceived ability to distinguish accurate news from misinformation - across different information channels during this high-stakes election cycle. Drawing on data from the Pew Research Center's American Trends Panel (Wave 155, September 2024, N = 9,360), we test three hypotheses: (H1) whether reliance on social media predicts lower epistemic self-efficacy compared to mainstream news sources; (H2) whether perceived exposure to inaccurate information mediates this relationship; and (H3) whether information fatigue moderates the cognitive burden of verification across platforms. Contrary to expectations rooted in algorithmic filtering theory, we find no significant differences in reported difficulty determining truth between social media and mainstream news users. Instead, epistemic burden is driven by demographics (age, education) and universal information fatigue, suggesting a "leveling" of the information landscape during periods of extreme volatility. This finding challenges platform-deterministic theories and suggests that interventions to support informed citizenship must address cognitive resilience and attention management rather than platform choice alone. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_20679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Signal, Noise, and Burnout: A Human-Information Interaction Analysis of Voter Verification in a High-Volatility Environment Lee, Kijung Social and Information Networks Human-Computer Interaction The 2024 U.S. Presidential Election unfolded within an information environment of unprecedented volatility, challenging citizens to navigate a torrent of rapidly evolving, often contradictory information while determining what to believe. This study investigates the cognitive mechanisms underlying epistemic self-efficacy - the perceived ability to distinguish accurate news from misinformation - across different information channels during this high-stakes election cycle. Drawing on data from the Pew Research Center's American Trends Panel (Wave 155, September 2024, N = 9,360), we test three hypotheses: (H1) whether reliance on social media predicts lower epistemic self-efficacy compared to mainstream news sources; (H2) whether perceived exposure to inaccurate information mediates this relationship; and (H3) whether information fatigue moderates the cognitive burden of verification across platforms. Contrary to expectations rooted in algorithmic filtering theory, we find no significant differences in reported difficulty determining truth between social media and mainstream news users. Instead, epistemic burden is driven by demographics (age, education) and universal information fatigue, suggesting a "leveling" of the information landscape during periods of extreme volatility. This finding challenges platform-deterministic theories and suggests that interventions to support informed citizenship must address cognitive resilience and attention management rather than platform choice alone. |
| title | Signal, Noise, and Burnout: A Human-Information Interaction Analysis of Voter Verification in a High-Volatility Environment |
| topic | Social and Information Networks Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.20679 |