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Author
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Silva, D. E., Chen, C., & Zhu, Y.
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Year
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2024
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Publisher
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New Media & Society, 26(5), 2992-3017.
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Abstract
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The article examines how widespread algorithmic decision-making systems shape digital media while remaining difficult for the public to critically evaluate. Drawing on the theory of attitude–behavior consistency from political communication, the authors develop a framework to foster algorithmic literacy and encourage more informed public attitudes toward algorithms. They design and test an intervention that combines algorithmic education with personalized user experiences to assess its impact. The findings show that both components together shape attitudes toward algorithms, but the effectiveness of the intervention varies depending on individuals’ patterns of technology use.
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Language
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English