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.
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CITATION STYLE
Jia, C., & Gwizdka, J. (2020). An Eye-Tracking Study of Differences in Reading Between Automated and Human-Written News. In Lecture Notes in Information Systems and Organisation (Vol. 43, pp. 100–110). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60073-0_12
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