Source Credibility Matters: Does Automated Journalism Inspire Selective Exposure?

Chenyan Jia, Thomas J. Johnson


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.


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

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