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Leveraging social media networks for classification

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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.

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Authors and Affiliations

  1. Advertising Sciences, Yahoo! Labs, Santa Clara, CA, 95054, USA

    Lei Tang

  2. Computer Science and Engineering, Arizona State University, Tempe, AZ, 85287, USA

    Huan Liu

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  1. Lei Tang
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  2. Huan Liu
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Correspondence to Lei Tang.

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Responsible editor: Johannes Gehrke.

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Tang, L., Liu, H. Leveraging social media networks for classification. Data Min Knowl Disc 23, 447–478 (2011). https://doi.org/10.1007/s10618-010-0210-x

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