Understanding User Attention and Engagement in Online News Reading
Dmitry Lagun,Mounia Lalmas +1 more
- pp 113-122
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TLDR
The proposed user engagement classes provide clear and interpretable taxonomy of user engagement with online news, and are defined based on amount of time user spends on the page, proportion of the article user actually reads and the amount of interaction users performs with the comments.Abstract:Â
Prior work on user engagement with online media identified web page dwell time as a key metric reflecting level of user engagement with online news articles. While on average, dwell time gives a reasonable estimate of user experience with a news article, it is not able to capture important aspects of user interaction with the page, such as how much time a user spends reading the article vs. viewing the comment posted by other users, or the actual proportion of article read by the user. In this paper, we propose a set of user engagement classes along with new user engagement metrics that, unlike dwell time, more accurately reflect user experience with the content. Our user engagement classes provide clear and interpretable taxonomy of user engagement with online news, and are defined based on amount of time user spends on the page, proportion of the article user actually reads and the amount of interaction users performs with the comments. Moreover, we demonstrate that our metrics are relatively easier to predict from the news article content, compared to the dwell time, making optimization of user engagement more attainable goal.read more
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View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations
TL;DR: It is shown that one can predict if an article will be publicly shared to another platform by individuals with precision of approximately 80% and has implications for news organizations desiring to increase and to prioritize types of user engagement.
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Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading
Hongyu Lu,Min Zhang,Shaoping Ma +2 more
TL;DR: This work sheds light on the understanding of user click behaviors and provides a method for better estimating user interest and satisfaction, and builds an effective model to predict whether the user actually likes the clicked news.
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All Those Wasted Hours: On Task Abandonment in Crowdsourcing
Lei Han,Kevin Roitero,Ujwal Gadiraju,Cristina Sarasua,Alessandro Checco,Eddy Maddalena,Gianluca Demartini +6 more
TL;DR: This paper conducts the first investigation into the phenomenon of task abandonment, the act of workers previewing or beginning a task and deciding not to complete it and shows how task abandonment may have strong implications on the use of collected data (for example, on the evaluation of IR systems).
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Personalised News and Blog Recommendations based on User Location, Facebook and Twitter User Profiling
TL;DR: A prototype mobile app that provides out-of-the-box personalised content recommendations to its users by leveraging and combining the user's location, their Facebook and/or Twitter feed and their in-app actions to automatically infer their interests.
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Towards Measuring and Inferring User Interest from Gaze
TL;DR: The relationship between mobile users' implicit interest inferred from attention metrics, such as eye gaze or viewport time, and explicit interest expressed by users is investigated and a prediction model is built that is able to infer a user's interest ratings from the the non-click actions of the user.
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