Twitter Session Analytics: Profiling Users’ Short-Term Behavioral Changes

  • Farshad KootiEmail author
  • Esteban Moro
  • Kristina Lerman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)


Human behavior shows strong daily, weekly, and monthly patterns. In this work, we demonstrate online behavioral changes that occur on a much smaller time scale: minutes, rather than days or weeks. Specifically, we study how people distribute their effort over different tasks during periods of activity on the Twitter social platform. We demonstrate that later in a session on Twitter, people prefer to perform simpler tasks, such as replying and retweeting others’ posts, rather than composing original messages, and they also tend to post shorter messages. We measure the strength of this effect empirically and statistically using mixed-effects models, and find that the first post of a session is up to 25 % more likely to be a composed message, and 10–20 % less likely to be a reply or retweet. Qualitatively, our results hold for different populations of Twitter users segmented by how active and well-connected they are. Although our work does not resolve the mechanisms responsible for these behavioral changes, our results offer insights for improving user experience and engagement on online social platforms.


Online Social Network Twitter User Activity Session Social Platform Index Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.USC Information Sciences InstituteMarina Del ReyUSA
  2. 2.Universidad Carlos III de MadridMadridSpain

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