Interactive Analytics in Social Media

Living reference work entry



Interactive Analytics in Social Media is a multistep process through which an analyst refines his/her understanding of users and their actions in social media. Interactive analytics in social media is helpful in data science where analysts do not necessarily know what to look for. It is a recent research field of large practical importance, with many open challenges. Interactive analytics in social media could be formulated in different ways including exploration under constraints such as minimizing the analyst’s time, maximizing the diversity of returned results, optimizing coverage of the input, or minimizing the number of exploration steps.

The main benefit of interactive analytics in social media is that it virtually sits on top of most social media analytics techniques as an exploratory layer that enables the gradual understanding of underlying datasets. It is thus essential that interactive analytics allows analysts...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Bhuiyan M, Mukhopadhyay S, Hasan MA. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management; 2012.Google Scholar
  2. 2.
    Boley M, Mampaey M, Kang B, Tokmakov P, Wrobel S. One click mining: interactive local pattern discovery through implicit preference and performance learning. In: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics; 2013.Google Scholar
  3. 3.
    Dong X, Xuehua S, Qiaozhu M, Jiawei H. Discovering interesting patterns through user’s interactive feedback. Knowledge discovery and data mining. New York: ACM; 2006.Google Scholar
  4. 4.
    Dzyuba V, van Leeuwen M, Nijssen S, De Raedt L. Active preference learning for ranking patterns. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2013); 2013.Google Scholar
  5. 5.
    Fekete JD. Solving problems with visual analytics book, chapter 6 In: Infrastructure. Keim D, Kohlhammer J, Ellis G, Mansmann F, editors.
  6. 6.
    Geng L, Hamilton HJ. Interestingness measures for data mining: a survey. ACM Comput Surv. (CSUR) 2006;38(3):1–32.Google Scholar
  7. 7.
    Goethals B, Moens S, Vreeken J. MIME: a framework for interactive visual pattern mining. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2011.Google Scholar
  8. 8.
    Instant Interactive Data Mining Workshop, ECML-PKDD workshops; 2012.
  9. 9.
    Interactive Data Exploration and Analytics Workshop (IDEA), KDD Workshops; 2013.
  10. 10.
    van Leeuwen M. Interactive data exploration using pattern mining. In: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics; 2014.Google Scholar

Authors and Affiliations

  1. 1.Laboratoire d’Informatique de GrenobleCNRS and LIGGrenobleFrance
  2. 2.LIG (Laboratoire d’Informatique de Grenoble), HADAS teamUniversité Joseph FourierGrenobleFrance
  3. 3.Laboratoire d’Informatique de GrenobleSaint-Martin d’HèresFrance

Section editors and affiliations

  • Fatma Özcan
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA