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Data Mining and Knowledge Discovery

, Volume 23, Issue 3, pp 447–478 | Cite as

Leveraging social media networks for classification

  • Lei Tang
  • Huan Liu
Article

Abstract

Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors’ latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.

Keywords

Social media Social network analysis Relational learning Within-network classification Collective inference 

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Copyright information

© The Author(s) 2011

Authors and Affiliations

  1. 1.Advertising SciencesYahoo! LabsSanta ClaraUSA
  2. 2.Computer Science and EngineeringArizona State UniversityTempeUSA

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