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

Conference paper

DOI: 10.1007/978-3-319-47874-6_6

Volume 10047 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Kooti F., Moro E., Lerman K. (2016) Twitter Session Analytics: Profiling Users’ Short-Term Behavioral Changes. In: Spiro E., Ahn YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science, vol 10047. Springer, Cham

Abstract

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.

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