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

In: Information Systems and Neuroscience

Author

Listed:
  • Chenyan Jia

    (University of Texas at Austin)

  • Jacek Gwizdka

    (University of Texas at Austin)

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.

Suggested Citation

  • Chenyan Jia & Jacek Gwizdka, 2020. "An Eye-Tracking Study of Differences in Reading Between Automated and Human-Written News," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Thomas (ed.), Information Systems and Neuroscience, pages 100-110, Springer.
  • Handle: RePEc:spr:lnichp:978-3-030-60073-0_12
    DOI: 10.1007/978-3-030-60073-0_12
    as

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