Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Structure Analytics in Social Media

  • Sihem Amer-YahiaEmail author
  • Mahashweta Das
  • Gautam Das
  • Saravanan Thirumuruganathan
  • Cong Yu
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80709


Aggregate analytics in social media; Exploratory mining in social media; User-generated content analysis


Structure analytics in social media is the process of discovering the structure of the relationships emerging from social media use, by leveraging the rich metadata associated with items and users in online sites. It focuses on identifying the users involved, the activities they undertake, the actions they perform, and the items they create and interact with. Example items can be movies, restaurants, entities, and Web pages. The objective of structure analytics in social media is to identify interesting patterns in large amounts of user-generated content such as product reviews, rating, forums, and social media conversations and use that knowledge in subsequent actions. An example mining task is finding groups of reviewers who have similar feedback (such as high ratings) for similar (or diverse) sets of items (such as movies by the same director). Unlike...

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

Recommended Reading

  1. 1.
    Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3(4/5):993–1022.zbMATHGoogle Scholar
  2. 2.
    Das M, Amer-Yahia S, Das G, Mri CY. Meaningful interpretations of collaborative ratings. Proc VLDB Endow. 2011;4(11):1063–74.Google Scholar
  3. 3.
    Das M, Thirumuruganathan S, Amer-Yahia S, Das G, Yu C. Who tags what? An analysis framework. Proc VLDB Endow. 2012;5(11):1567–78.CrossRefGoogle Scholar
  4. 4.
    Das M, Thirumuruganathan S, Amer-Yahia S, Das G, Yu C. An expressive framework and efficient algorithms for the analysis of collaborative tagging. VLDB J. 2014;23(2):201–26.CrossRefGoogle Scholar
  5. 5.
    Fortunato S. Community detection in graphs. Phys Rep. 2010;486(3):75–174.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sarawagi S. Explaining differences in multidimensional aggregates. In: Proceedings of the 25th International Conference on Very Large Data Bases; 1999. p. 2–53.Google Scholar
  7. 7.
    Sarawagi S, Agrawal R, Megiddo N. Discovery-driven exploration of OLAP data cubes. Springer; 1998.Google Scholar
  8. 8.
    Sathe G, Sarawagi S. Intelligent rollups in multidimensional olap data. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 531–40.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sihem Amer-Yahia
    • 1
    • 2
    Email author
  • Mahashweta Das
    • 3
  • Gautam Das
    • 4
  • Saravanan Thirumuruganathan
    • 4
    • 5
  • Cong Yu
    • 6
  1. 1.CNRSUniv. Grenoble AlpsGrenobleFrance
  2. 2.Laboratoire d’Informatique de GrenobleCNRS-LIGSaint Martin-d’HèresFrance
  3. 3.Visa ResearchPalo AltoUSA
  4. 4.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  5. 5.Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
  6. 6.Google ResearchNew YorkUSA

Section editors and affiliations

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