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Understanding online groups through social media

Authors

  • Geoffrey Barbier,

    Corresponding author
    1. School of Computing, Informatics, and Decision Systems Engineering, Data Mining and Machine Learning (DMML) Laboratory, Arizona State University, Tempe, AZ, USA
    • School of Computing, Informatics, and Decision Systems Engineering, Data Mining and Machine Learning (DMML) Laboratory, Arizona State University, Tempe, AZ, USA
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  • Lei Tang,

    1. School of Computing, Informatics, and Decision Systems Engineering, Data Mining and Machine Learning (DMML) Laboratory, Arizona State University, Tempe, AZ, USA
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  • Huan Liu

    1. School of Computing, Informatics, and Decision Systems Engineering, Data Mining and Machine Learning (DMML) Laboratory, Arizona State University, Tempe, AZ, USA
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Abstract

Multiple fields including sociology, anthropology, and business are interested in understanding group behavior. Applying data mining techniques to social media can help provide insights into group behavior and divulge a group's characteristics by identifying a group, developing a profile for a group, revealing the sentiment of a group, and detailing a group's composition. The ability to accomplish these tasks has practical business and scientific applications such as understanding customers better and providing new insights into influence propagation, as well as the ability to accurately categorize groups over time. This paper highlights some ongoing research efforts aiming at understanding groups through social media. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 330–338 DOI: 10.1002/widm.37

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