TITLE: Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models 

ABSTRACT: A single digital newsletter usually contains many messages (regions). Users'
reading time spent on, and read level (skip/skim/read-in-detail) of each
message is important for platforms to understand their users' interests,
personalize their contents, and make recommendations. Based on accurate but
expensive-to-collect eyetracker-recorded data, we built models that predict
per-region reading time based on easy-to-collect Javascript browser tracking
data.
  With eye-tracking, we collected 200k ground-truth datapoints on participants
reading news on browsers. Then we trained machine learning and deep learning
models to predict message-level reading time based on user interactions like
mouse position, scrolling, and clicking. We reached 27\% percentage error in
reading time estimation with a two-tower neural network based on user
interactions only, against the eye-tracking ground truth data, while the
heuristic baselines have around 46\% percentage error. We also discovered the
benefits of replacing per-session models with per-timestamp models, and adding
user pattern features. We concluded with suggestions on developing
message-level reading estimation techniques based on available data.
A single digital newsletter usually contains many messages (regions). Users'
reading time spent on, and read level (skip/skim/read-in-detail) of each
message is important for platforms to understand their users' interests,
personalize their contents, and make recommendations. Based on accurate but
expensive-to-collect eyetracker-recorded data, we built models that predict
per-region reading time based on easy-to-collect Javascript browser tracking
data.
  With eye-tracking, we collected 200k ground-truth datapoints on participants
reading news on browsers. Then we trained machine learning and deep learning
models to predict message-level reading time based on user interactions like
mouse position, scrolling, and clicking. We reached 27\% percentage error in
reading time estimation with a two-tower neural network based on user
interactions only, against the eye-tracking ground truth data, while the
heuristic baselines have around 46\% percentage error. We also discovered the
benefits of replacing per-session models with per-timestamp models, and adding
user pattern features. We concluded with suggestions on developing
message-level reading estimation techniques based on available data.
Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models
http://arxiv.org/abs/2306.07455v1