Skip to main content

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

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10047)

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.

Keywords

  • 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.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-47874-6_6
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-47874-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.

Notes

  1. 1.

    http://weka.sourceforge.net/doc.packages/XMeans/weka/clusterers/XMeans.html.

References

  1. Aledavood, T., López, E., Roberts, S.G., Reed-Tsochas, F., Moro, E., Dunbar, R.I., Saramäki, J.: Daily rhythms in mobile telephone communication. PloS One 10(9), e0138098 (2015)

    CrossRef  Google Scholar 

  2. Baumeister, R.F., Bratslavsky, E., Muraven, M., Tice, D.M.: Ego depletion: is the active self a limited resource? J. Pers. Soc. Psychol. 74(5), 1252 (1998)

    CrossRef  Google Scholar 

  3. Baumeister, R.F., Sparks, E.A., Stillman, T.F., Vohs, K.D.: Free will in consumer behavior: self-control, ego depletion, and choice. J. Consum. Psychol. 18(1), 4–13 (2008)

    CrossRef  Google Scholar 

  4. Baumeister, R.F., Vohs, K.D.: Self-regulation, ego depletion, and motivation. Soc.Pers. Psychol. Compass 1(1), 115–128. http://dx.doi.org/10.1111/j.1751-9004.2007.00001.x

    Google Scholar 

  5. Daoud, M., Tamine-Lechani, L., Boughanem, M., Chebaro, B.: A session based personalized search using an ontological user profile. In: Proceedings of the 2009 ACM Symposium on Applied Computing, pp. 1732–1736. ACM (2009)

    Google Scholar 

  6. Eickhoff, C., Teevan, J., White, R., Dumais, S.: Lessons from the journey: a query log analysis of within-session learning. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 223–232. ACM (2014)

    Google Scholar 

  7. Gailliot, M.T., Baumeister, R.F., DeWall, C.N., Maner, J.K., Plant, E.A., Tice, D.M., Brewer, L.E., Schmeichel, B.J.: Self-control relies on glucose as a limited energy source: willpower is more than a metaphor. J. Pers. Soc. Psychol. 92(2), 325 (2007)

    CrossRef  Google Scholar 

  8. Ghosh, R., Surachawala, T., Lerman, K.: Entropy-based classification of retweeting activity on twitter. In: Proceedings of KDD Workshop on Social Network Analysis (SNA-KDD), August 2011

    Google Scholar 

  9. Golder, S.A., Macy, M.W.: Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051), 1878–1881 (2011)

    CrossRef  Google Scholar 

  10. Golder, S.A., Wilkinson, D.M., Huberman, B.A.: Rhythms of social interaction: Messaging within a massive online network. In: Communities and Technologies 2007, pp. 41–66. Springer (2007)

    Google Scholar 

  11. Goševa-Popstojanova, K., Singh, A.D., Mazimdar, S., Li, F.: Empirical characterization of session-based workload and reliability for web servers. Empirical Softw. Eng. 11(1), 71–117 (2006)

    CrossRef  Google Scholar 

  12. Grinberg, N., Dow, P.A., Adamic, L.A., Naaman, M.: Extracting diurnal patterns of real world activity from social media. In: CHI (2016)

    Google Scholar 

  13. Grinberg, N., Naaman, M., Shaw, B., Lotan, G.: Extracting diurnal patterns of real world activity from social media. In: ICWSM (2013)

    Google Scholar 

  14. Healy, A.F., Kole, J.A., Buck-Gengle, C.J., Bourne, L.E.: Effects of prolonged work on data entry speed and accuracy. J. Exp. Psychol. Appl. 10(3), 188–199. http://view.ncbi.nlm.nih.gov/pubmed/15462620

  15. Huang, J., Efthimiadis, E.N.: Analyzing and evaluating query reformulation strategies in web search logs. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 77–86. ACM (2009)

    Google Scholar 

  16. Jin, L., Chen, Y., Wang, T., Hui, P., Vasilakos, A.V.: Understanding user behavior in online social networks: a survey. IEEE Commun. Mag. 51(9), 144–150 (2013)

    CrossRef  Google Scholar 

  17. Jones, R., Klinkner, K.L.: Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 699–708. ACM (2008)

    Google Scholar 

  18. Kooti, F., Lerman, K., Aiello, L.M., Grbovic, M., Djuric, N., Radosavljevic, V.: Portrait of an online shopper: understanding and predicting consumer behavior. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM 2016), San Francisco, USA, February 2016

    Google Scholar 

  19. Kouchaki, M., Smith, I.H.: The morning morality effect the influence of time of day on unethical behavior. Psychol. Sci. 25(1), 95–102 (2013). 0956797613498099

    Google Scholar 

  20. Kumar, R., Tomkins, A.: A characterization of online browsing behavior. In: Proceedings of the 19th International Conference on World Wide Web, pp. 561–570. ACM (2010)

    Google Scholar 

  21. Llorente, A., Garcia-Herranz, M., Cebrian, M., Moro, E.: Social media fingerprints of unemployment. PloS One 10(5), e0128692 (2015)

    CrossRef  Google Scholar 

  22. Muraven, M., Tice, D., Baumeister, R.: Self-control as a limited resource: regulatory depletion patterns. J. Pers. Soc. Psychol. 74(3), 774 (1998)

    CrossRef  Google Scholar 

  23. Muraven, M., Baumeister, R.F.: Self-regulation and depletion of limited resources: does self-control resemble a muscle? Psychol. Bull. 126(2), 247 (2000)

    CrossRef  Google Scholar 

  24. Rose, D.E., Levinson, D.: Understanding user goals in web search. In: Proceedings of the 13th International Conference on World Wide Web, pp. 13–19. ACM (2004)

    Google Scholar 

  25. Saramäki, J., Moro, E.: From seconds to months: an overview of multi-scale dynamics of mobile telephone calls. Eur. Phys. J. B 88(6), 1–10 (2015)

    CrossRef  Google Scholar 

  26. Smith, B.R., Linden, G.D., Zada, N.K.: Content personalization based on actions performed during a current browsing session, uS Patent 6,853,982, 8 February 2005

    Google Scholar 

  27. Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A framework for the evaluation of session reconstruction heuristics in web-usage analysis. Inf. J. Comput. 15(2), 171–190 (2003)

    CrossRef  MATH  Google Scholar 

  28. Teevan, J., Ramage, D., Morris, M.R.: # twittersearch: a comparison of microblog search and web search. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 35–44. ACM (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Farshad Kooti .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

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. https://doi.org/10.1007/978-3-319-47874-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47874-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47873-9

  • Online ISBN: 978-3-319-47874-6

  • eBook Packages: Computer ScienceComputer Science (R0)