Source Credibility Matters: Does Automated Journalism Inspire Selective Exposure?

Authors

  • Chenyan Jia The University of Texas at Austin
  • Thomas J. Johnson The University of Texas at Austin

Keywords:

automated journalism, algorithm, message credibility, selective avoidance, selective exposure, source credibility

Abstract

To examine whether selective exposure occurs when people read news attributed to an algorithm author, this study conducted a 2 (author attribution: human or algorithm) × 3 (article attitude: attitude-consistent news, attitude-challenging news, or neutral story) × 2 (article topic: gun control or abortion) mixed-design online experiment (N = 351). By experimentally manipulating the attribution of authorship, this study found that selective exposure and selective avoidance were practiced when the news article was declared to be written by algorithms. Results revealed that people were more likely to select attitude-consistent news rather than attitude-challenging news and rated attitude consistent news stories as more credible than attitude challenging news for stories purportedly written by both algorithms and human journalists. For attitude-consistent gun-rights stories, people were more likely to expose themselves to human attribution stories rather than algorithmic attribution stories. Results also showed that source credibility partially mediated the influence of issue partisanship on selective exposure for gun stories. This study bears implications on the selective exposure theory and the emerging field of automated journalism.

Author Biographies

Chenyan Jia, The University of Texas at Austin

Contact informationChenyan Jia (corresponding author, Ph.D.student)Email: chenyanjia@utexas.eduPhone: 512-718-2244ORCID ID: https://orcid.org/0000-0002-8407-9224Author bioChenyan Jia is a doctoral student in the School of Journalism at the University of Texas at Austin. Her research interests include algorithmic bias, automated journalism, and human-computer interaction. She is especially interested in how algorithms and other digital technologies affect journalism and media. She has published several peer-reviewed papers on automated journalism and social computing.

Thomas J. Johnson, The University of Texas at Austin

Thomas J. Johnson is the Amon G. Carter Jr. Centennial Professor in the School of Journalism at the University of Texas, Austin. Johnson's work has focused on the credibility of the Internet and its components, selective exposure and the Internet, and the agenda-building role of the media and how factors influence journalistic frames. Johnson has 65 refereed journal articles published or in press, 21 book chapters and more than 100 papers at international, national and regional conferences. He has more than 22 published journal articles about selective exposure and credibility.

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Published

2021-08-14

Issue

Vol. 15 (2021)

Section

Articles

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