Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agarwal,, Abhijit Mishra, Pushpak Bhattacharyya

TL;DR
This paper demonstrates that gaze behaviour can effectively predict text quality ratings by modeling organization, coherence, and cohesion, with improved accuracy when the reader fully understands the text.
Contribution
It introduces a novel approach combining gaze behaviour with textual features to predict subjective text quality ratings.
Findings
Gaze features improve prediction accuracy of text quality.
Full understanding correlates with better prediction agreement.
Gaze behaviour captures subjective aspects like coherence and cohesion.
Abstract
Predicting a reader's rating of text quality is a challenging task that involves estimating different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are better handled using cognitive information. One such source of cognitive information is gaze behaviour. In this paper, we show that gaze behaviour does indeed help in effectively predicting the rating of text quality. To do this, we first model text quality as a function of three properties - organization, coherence and cohesion. Then, we demonstrate how capturing gaze behaviour helps in predicting each of these properties, and hence the overall quality, by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. We also hypothesize that if a reader has fully understood the text, the corresponding gaze behaviour would give a better indication of…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Topic Modeling · Information Retrieval and Search Behavior
