Predicting Expectancy Violations Using Eye-Tracking Features: A Machine Learning Approach

Web searchers continuously form expectations about document content based on snippets and titles, yet when these expectations are violated, their attention and satisfaction are disrupted. Detecting such expectancy violations in real-time can enable adaptive, user-aware Web systems that respond to cognitive mismatches. This paper investigates whether eye-tracking features can predict expectancy violations during Web search and identifies which temporal aspects of attention carry predictive information. Using data from 34 participants performing controlled search tasks, we extracted four gaze metrics, i.e. time to first fixation, total fixation duration, number of fixations, and mean fixation duration, and trained machine learning models using a leave-one-participant-out cross-validation approach. Sustained attention features, particularly total fixation duration and number of fixations, predicted expectancy violations with 62.6% accuracy (p = .008), while initial attention metrics performed at chance. The results reveal that expectancy violations manifest through extended visual engagement rather than immediate orienting responses. The findings of this work contribute to the theoretical understanding of user–system interaction on the Web and provide a foundation for adaptive retrieval interfaces capable of detecting cognitive surprise and delivering timely support.