Skip to content (access key 's')
Logo of Technion
Logo of CS Department


Understanding Reader Backtracking Behavior in Online News Articles
event speaker icon
Uzi Smadja, M.Sc. Thesis Seminar
event date icon
Tuesday, 29.1.2019, 12:30
event location icon
Taub 401
Rich engagement data can shed light on how people interact with online content and how such interactions may be determined by the content of the page. In this work, we investigate a specific type of interaction, backtracking, which refers to the action of scrolling back in a browser while reading an online news article. We leverage a dataset of close to 700K instances of more than 15K readers interacting with online news articles, in order to characterize and predict backtracking behavior. We first define different types of backtracking actions. We then show that "full" backtracks, where the readers eventually return to the spot at which they left the text, can be predicted by using features that have been shown to relate to text readability. This finding highlights the relationship between backtracking and readability and suggests that backtracking could help assess readability of content at scale.
[Back to the index of events]