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Socio-Semantic Analysis

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Social Multimedia Signals

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Abstract

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.

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Notes

  1. 1.

    The pure list of words collected from DBpedia is around 1 billion, however you can pre-process common words into representative concepts (hierarchy) for ease of graph manipulation.

  2. 2.

    The actual query performed on Feb 22nd, 2012: [egypt, +tahrir+army+revolution+police+egyptian+watching+world+support+jail]. Use ‘+’ to prevent Google from using synonyms or lexical variants of the topical words.

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Authors and Affiliations

  1. Betaworks, New York, NY, USA

    Suman Deb Roy

  2. Department of Computer Science, University of Missouri, Columbia, MO, USA

    Wenjun Zeng

Authors
  1. Suman Deb Roy
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  2. Wenjun Zeng
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Correspondence to Suman Deb Roy .

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Roy, S.D., Zeng, W. (2015). Socio-Semantic Analysis. In: Social Multimedia Signals. Springer, Cham. https://doi.org/10.1007/978-3-319-09117-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-09117-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09116-7

  • Online ISBN: 978-3-319-09117-4

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