AI as an Apolitical Referee
Using Alternative Sources to Decrease Partisan Biases in the Processing of Fact-Checking Messages
Bibliographic Data
| ID | 22010664 |
|---|---|
| Authors | M Chung (0000-0001-6290-859X, Northeastern University, corresponding author), Won-Ki Moon (0000-0003-4959-5343, The University of Texas at Austin, corresponding author), M Jones-Jang (0000-0003-3935-7421, Boston College) |
| Year | 2024 |
| Volume | 12 |
| Issue | 10 |
| Pages | 1548-1569 |
| Publication date | 2024-11-25 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Digital Journalism (JOURNAL) |
| Journal identifiers | ISSN: 2167-0811 • E-ISSN: 2167-082X |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/21670811.2023.2254820 |
| OpenAlex | W4386739079 |
| Language | EN |
| Citations received | 21 |
| References cited | 75 |
While fact-checking has received much attention as a tool to fight misinformation online, fact-checking efforts have yielded limited success in combating political misinformation due to partisans’ biased information processing. The efficacy of fact-checking often decreases, if not backfires, when the fact-checking messages contradict individual audiences’ political stance. To explore ways to minimize such politically biased processing of fact-checking messages, an online experiment (N = 645) examined how different source labels of fact-checking messages (human experts vs. AI vs. crowdsourcing vs. human experts-AI hybrid) influence partisans’ processing of fact-checking messages. Results showed that AI and crowdsourcing source labels significantly reduced motivated reasoning in evaluating the credibility of fact-checking messages whereas the partisan bias remained evident for the human experts and human experts-AI hybrid source labels
Computer security · Credibility · Crowdsourcing · Economics · Misinformation · Political science · Politics · Source credibility · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Law · Misinformation and Its Impacts · Psychology · Social Media and Politics · Social Psychology
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The Polarizing Effect of News Framing
Building a Stronger Casa
Man vs. Machine? The Impact of Algorithm Authorship on News Credibility
Motivated Fake News Perception
Do We Trust the Crowd? Effects of Crowdsourcing on Perceived Credibility of Online Health Information
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How Can We Fight Partisan Biases in the Covid-19 Pandemic? AI Source Labels on Fact-checking Messages Reduce Motivated Reasoning
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The Elusive Backfire Effect
Motivated Reasoning and Political Parties
The Influence of Partisan Motivated Reasoning on Public Opinion
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Motivated Reasoning about Public Performance
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Correcting Misperceptions About Genetically Modified Food on Social Media
Cultural cognition of scientific consensus
Selective Exposure to Populist Communication
The Partisan Brain
The case for motivated reasoning
Polarization and the Global Crisis of Democracy
Party over Pocketbook? How Party Cues Influence Opinion When Citizens Have a Stake in Policy
| Unique citing works | 21 |
|---|---|
| Citations per year | 10,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 18 |