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Faceted Browsing over Social Media

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7678)

Abstract

The popularity of social media as a medium for sharing information has made extracting information of interest a challenge. In this work we provide a system that can return posts published on social media covering various aspects of a concept being searched. We present a faceted model for navigating social media that provides a consistent, usable and domain-agnostic method for extracting information from social media. We present a set of domain independent facets and empirically prove the feasibility of mapping social media content to the facets we chose. Next, we show how we can map these facets to social media sites, living documents that change periodically to topics that capture the semantics expressed in them. This mapping is used as a graph to compute the various facets of interest to us. We learn a profile of the content creator, enable content to be mapped to semantic concepts for easy navigation and detect similarity among sites to either suggest similar pages or determine pages that express different views.

Keywords

  • Social Medium
  • Latent Dirichlet Allocation
  • Inverse Document Frequency
  • Word Cloud
  • Topic Extraction

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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Nambiar, U., Faruquie, T., Kumar, S., Morstatter, F., Liu, H. (2012). Faceted Browsing over Social Media. In: Srinivasa, S., Bhatnagar, V. (eds) Big Data Analytics. BDA 2012. Lecture Notes in Computer Science, vol 7678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35542-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-35542-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35541-7

  • Online ISBN: 978-3-642-35542-4

  • eBook Packages: Computer ScienceComputer Science (R0)