Encyclopedia of Database Systems

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

Structure Analytics in Social Media

  • Sihem Amer-Yahia
  • Mahashweta Das
  • Gautam Das
  • Saravanan Thirumuruganathan
  • Cong Yu
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_80709-1

Synonyms

Definition

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...

Keywords

Social Media Recommender System Data Cube Social Media Data Social Medium Analytics 
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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Recommended Reading

  1. 1.
    Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.MATHGoogle Scholar
  2. 2.
    Das M, Amer-Yahia S, Das G, Mri CY. Meaningful interpretations of collaborative ratings. Proc VLDB Endowment. 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 Endowment. 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: VLDB, vol. 99. Citeseer; 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. VLDB. 2001;1:531–40.Google Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Sihem Amer-Yahia
    • 1
  • Mahashweta Das
    • 2
  • Gautam Das
    • 3
  • Saravanan Thirumuruganathan
    • 3
  • Cong Yu
    • 4
  1. 1.Laboratoire d’Informatique de GrenobleCNRSGrenobleFrance
  2. 2.Visa ResearchPalo AltoUSA
  3. 3.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  4. 4.Google ResearchNew YorkUSA