Socio-Semantic Analysis

  • Suman Deb RoyEmail author
  • Wenjun Zeng


Over the last decade, two computational ideas have fundamentally disrupted how humans receive and consume information. Online Social Networks and Social Media revolutionized information diffusion in societies, compelling traditional media, advertising and technology companies to honor the wisdom of the crowds. This chapter argues that intelligent social media systems need a substantial understanding of the related semantics. The first step in using semantic data is to create a concept graph.  The purpose of this chapter is to utilize the power of semantic graphs in better understanding of social multimedia data. Principally, we want to use semantic graphs for two purposes: (1) categorize semantic textual information based on semantic graphs and (2) finding coherency of social topics (words that are part of the topics extracted from social streams) by projecting these words onto semantic graphs.


Resource Description Format Semantic Network Semantic Concept Concept Graph Social Topic 
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.


  1. 1.
    Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis Lectures on the Semantic Web: Theory and Technology, 1(1), 1–136.CrossRefGoogle Scholar
  2. 2.
    Mendes, P. N., Jakob, M., & Bizer, C. (2012). DBpedia: A multilingual cross-domain knowledge base. In LREC (pp. 1813–1817).Google Scholar
  3. 3.
    Fellbaum, C. (2010). Wordnet. In Theory and applications of ontology: computer applications (pp. 231–243). Springer Berlin.Google Scholar
  4. 4.
    Yao, X., & Van Durme, B. (2014). Information extraction over structured data: Question answering with freebase. In Proceedings of ACL.Google Scholar
  5. 5.
    Singhal, A. (2012). Introducing the knowledge graph: things, not strings. Official Google Blog, May.Google Scholar
  6. 6.
    Hitzler, P., Krotzsch, M., & Rudolph, S. (2011). Foundations of semantic web technologies. Boca Raton: CRC Press.Google Scholar
  7. 7.
    Newman, M. E. (2005). A measure of betweenness centrality based on random walks. Social networks, 27(1), 39–54.CrossRefGoogle Scholar
  8. 8.
    Newman, M. E. (2006). Modularity and community structure in networks. In Proceedings of the National Academy of Sciences, 103(23), 8577–8582.CrossRefGoogle Scholar
  9. 9.
    Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41.CrossRefGoogle Scholar
  10. 10.
    Cilibrasi, R. L., & Vitanyi, P. M. (2007). The google similarity distance. Knowledge and Data Engineering, IEEE Transactions on, 19(3), 370–383.Google Scholar
  11. 11.
    Naaman, M., Becker, H., & Gravano, L. (2011). Hip and trendy: Characterizing emerging trends on Twitter. Journal of the American Society for Information Science and Technology, 62(5), 902–918.CrossRefGoogle Scholar
  12. 12.
    Scholz, M., & Klinkenberg, R. (2005). An ensemble classifier for drifting concepts. In Proceedings of the Second International Workshop on Knowledge Discovery in Data Streams (pp. 53–64). Porto, Portugal.Google Scholar
  13. 13.
    Chang, J., Boyd-Graber, J. L., Gerrish, S., Wang, C., & Blei, D. M. (2009, December). Reading tea leaves: How humans interpret topic models. InNIPS (vol. 22, pp. 288–296).Google Scholar
  14. 14.
    Bouma, G. (2009). Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL, pp. 31–40.Google Scholar
  15. 15.
    Dredze, M., McNamee, P., Rao, D., Gerber, A., & Finin, T. (2010, August). Entity disambiguation for knowledge base population. In Proceedings of the 23rd international conference on computational linguistics (pp. 277–285).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.BetaworksNew YorkUSA
  2. 2.Department of Computer ScienceUniversity of MissouriColumbiaUSA

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