Prior work on user engagement with online news sites identified dwell time as a key engagement metric. Whereas on average, dwell time gives a reasonable estimate of user engagement with a news article, it does not capture user engagement with the news article at sub-document level nor it allows to measure the proportion of article read by the user.
In this paper, we analyze online news reading patterns using large-scale viewport data collected from 267,210 page views on 1,971 news articles on a major online news website. We propose four engagement metrics that, unlike dwell time, more accurately reflect how users engage with and attend to the news content. The four metrics capture different levels of engagement, ranging from bounce to complete, providing clear and interpretable characterizations of user engagement with online news. Furthermore, we develop a probabilistic model that combines both an article textual content and level of user engagement information in a joint model. In our experiments we show that our model, called TUNE, is able to predict future level of user engagement based on textual content alone and outperform currently available methods.
Slides of our paper presented at The 9th ACM International Conference on Web Search and Data Mining (WSDM 2016),
San Francisco, California, USA. February 22-25, 2016.