Understanding Short-term Changes in Online Activity Sessions
By Farshad Kooti, Karthik Subbian, Winter A. Mason, Lada A. Adamic, Kristina Lerman. 2017
11 citations recorded by the source corpus.
Online activity is characterized by regularities such as diurnal and weekly patterns, reflecting human circadian rhythms and work and leisure schedules. Using data from the online social networking site Facebook, we uncover temporal patterns at a much smaller time scale: within individual sessions. Longer sessions have different characteristics than shorter ones, and this distinction is already visible in the first minute of a person's session activity. This allows us to predict the ultimate length of his or her session and how much content the person will see. The length of the session and other factors are in turn predictive of when the individual will return. Within a session, the amount of time a person spends on different kinds of content depends on both the person's demographic attributes, such as age and the number of Facebook friends, and the length of the time elapsed since the start of the session. We also find that liking and commenting is very non-uniformly distributed between sessions. Predictions of session duration and activity can potentially be leveraged to more efficiently cache content, especially to mobile devices in places with poor communications infrastructure, in order to improve user online experience.
Subfields: Computer Science Applications, Statistical and Nonlinear Physics, Transportation
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Citations in the corpus
- Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018, background): “However, despite accumulating evidence that Simpson’s paradox affects inference of trends in social and behavioral data [1, 2, 15, 19], researchers do not routinely test for it in their studies.”
- Computational social scientist beware: Simpson’s paradox in behavioral data (2017, background): “Content consumption in social media A study of content consumption on a popular social networking site Facebook examined the time users devote to viewing each item in their social feed [14].”