Understanding User Attention and Engagement in Online News Reading.
WSDM(2016)
摘要
ABSTRACTPrior 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络