  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.
