An Exploration of Cursor tracking Data

TL;DR

This study investigates how cursor tracking data can reveal user behavior and characteristics on websites, demonstrating its potential for understanding user intent and hardware differences, but not engagement levels.

Contribution

It introduces a novel analysis of cursor data to differentiate user activities and identify hardware, expanding web analytics capabilities.

Findings

Cursor data can distinguish reading from information-seeking behavior.

Hardware type can be inferred from cursor movement patterns.

No correlation found between cursor data and user engagement.

Abstract

Cursor tracking data contains information about website visitors which may provide new ways to understand visitors and their needs. This paper presents an Amazon Mechanical Turk study where participants were tracked as they used modified variants of the Wikipedia and BBC News websites. Participants were asked to complete reading and information-finding tasks. The results showed that it was possible to differentiate between users reading content and users looking for information based on cursor data. The effects of website aesthetics, user interest and cursor hardware were also analysed which showed it was possible to identify hardware from cursor data, but no relationship between cursor data and engagement was found. The implications of these results, from the impact on web analytics to the design of experiments to assess user engagement, are discussed.