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Title: Understanding Reader Backtracking Behavior in Online News Articles
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 were previously 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.  more » « less
Award ID(s):
1840751
PAR ID:
10097546
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
The World Wide Web Conference 2019
Page Range / eLocation ID:
3237 to 3243
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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