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Updated: Jan 10, 2026
09:40
Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
Published on: November 15, 2014
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Platform-independent experiments on social media.
Jennifer Allen1,2, Joshua A Tucker2,3
1Department of Technology, Operations, and Statistics, NYU Stern School of Business, New York, NY, USA.
Science (New York, N.Y.)
|November 27, 2025
Summary
Artificial intelligence (AI) algorithms can change how people feel about political groups. These changes in AI tools may increase dislike between political parties.
Area of Science:
- Computer Science
- Political Science
- Social Science
Background:
- Algorithms increasingly shape online information exposure.
- Understanding the impact of algorithmic changes on political attitudes is crucial.
Purpose of the Study:
- To investigate how modifications in artificial intelligence algorithms affect partisan animosity.
- To analyze the relationship between AI-driven content curation and political polarization.
Main Methods:
- Simulated changes to social media algorithms.
- Measured changes in user-reported partisan animosity.
- Utilized computational social science techniques.
Main Results:
- Algorithmic modifications demonstrated a significant influence on partisan animosity levels.
- Specific algorithm changes were correlated with increased negative sentiment towards opposing political groups.
- The study identified key algorithmic features driving polarization.
Conclusions:
- Artificial intelligence tools and their underlying algorithms can be leveraged to modulate partisan animosity.
- Future research should explore ethical considerations and mitigation strategies for AI-driven political polarization.
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