Understanding Short-term Changes in Online Activity Sessions. Kooti, F., Subbian, K., Mason, W., Adamic, L., & Lerman, K. In Proceedings of the 26th International World Wide Web Conference (Companion WWW2017), 2017.
abstract   bibtex   
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.
@INPROCEEDINGS{KootiA2017www,
  author =       {Farshad Kooti and Karthik Subbian and Winter Mason and Lada Adamic and Kristina Lerman},
  title =        {Understanding Short-term Changes in Online Activity Sessions},
  booktitle =    {Proceedings of the 26th International World Wide Web Conference (Companion WWW2017)},
  year =         {2017},
  pages =        {},
  publisher =    {},
  abstract =     {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.},
  keywords =     {social-dynamics, cognitive-depletion},
}
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