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

  • Chenyan JiaEmail author
  • Jacek Gwizdka
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 43)

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 

Notes

Acknowledgements

We thank Lan Li, who is a master student in the School of Information at the University of Texas at Austin, for contributing to the data collection.

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of JournalismUniversity of Texas at AustinAustinUSA
  2. 2.School of InformationUniversity of Texas at AustinAustinUSA

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