An Eye-Tracking Study of Differences in Reading Between Automated and Human-Written News

Chenyan Jia () and Jacek Gwizdka ()
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Chenyan Jia: University of Texas at Austin
Jacek Gwizdka: University of Texas at Austin

A chapter in Information Systems and Neuroscience, 2020, pp 100-110 from Springer

Abstract: Abstract An eye-tracking experiment (N = 24) was conducted to study differences in reading between automated and human-written news. This work adopted expectation-confirmation theory to examine readers’ prior expectations and actual perceptions of both human-written news and automated news. Results revealed that nine eye-tracking variables were significantly different when people read automated news vs. human-written news. Findings also showed promising classification results of 31 eye-tracking-derived features. Self-reported results showed that the readability of human-written news was perceived as significantly higher than that of automated news.

Keywords: Automated journalism; Human-written news; Eye-tracking; Expectation-confirmation theory; Readability (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-60073-0_12

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DOI: 10.1007/978-3-030-60073-0_12

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