A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
Kayhan Latifzadeh, Jacek Gwizdka, Luis A. Leiva

TL;DR
This paper introduces a comprehensive dataset combining eye-tracking, mouse movements, and SERP data from 47 users to improve understanding of user attention and behavior on search engine results pages.
Contribution
It provides a novel, multi-modal dataset with objective eye-tracking data and detailed SERP information, addressing limitations of self-reported labels in user behavior studies.
Findings
Baseline classification experiments demonstrate dataset utility.
Objective eye-tracking data offers more accurate attention insights.
Dataset supports diverse future research on search behavior.
Abstract
We contribute a comprehensive dataset to study user attention and purchasing behavior on Search Engine Result Pages (SERPs). Previous work has relied on mouse movements as a low-cost large-scale behavioral proxy but also has relied on self-reported ground-truth labels, collected at post-task, which can be inaccurate and prone to biases. To address this limitation, we use an eye tracker to construct an objective ground-truth of continuous visual attention. Our dataset comprises 2,776 transactional queries on Google SERPs, collected from 47 participants, and includes: (1) HTML source files, with CSS and images; (2) rendered SERP screenshots; (3) eye movement data; (4) mouse movement data; (5) bounding boxes of direct display and organic advertisements; and (6) scripts for further preprocessing the data. In this paper we provide an overview of the dataset and baseline experiments…
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