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Leader identification in an online health community for cancer survivors: a social network-based classification approach

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Abstract

Online health communities (OHCs) are an important source of social support for cancer survivors and their informal caregivers. This research attempted to identify leaders in a popular online forum for cancer survivors and caregivers using classification techniques. We first extracted user features from many different perspectives, including contributions, network centralities, and linguistic features. Based on these features, we leveraged the structure of the social network among users and generated new neighborhood-based and cluster-based features. Classification results revealed that these features are discriminative for leader identification. Using these features, we developed a hybrid approach based on an ensemble classifier that performs better than many traditional metrics. This research has implications for understanding and managing OHCs.

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Notes

  1. As one-class SVM only provides binary decisions, we specify the “outlier” ratio in the classification process, so that the classifier identifies K influential users.

  2. For esthetic purposes, the threshold of 15 was chosen to better illustrate the relationship between discussion boards. A lower threshold will increase the number of edges in the figure and a higher threshold will make the network sparser.

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Authors and Affiliations

  1. Department of Management Sciences, Tippie College of Business, The University of Iowa, S224 PBB, Iowa City, IA, 52242, USA

    Kang Zhao

  2. American Cancer Society, Atlanta, GA, 30303, USA

    Greta E. Greer & Kenneth Portier

  3. College of Information Sciences and Technology, Penn State University, University Park, PA, 16802, USA

    John Yen & Prasenjit Mitra

Authors
  1. Kang Zhao
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  2. Greta E. Greer
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  3. John Yen
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  4. Prasenjit Mitra
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  5. Kenneth Portier
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Correspondence to Kang Zhao.

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Zhao, K., Greer, G.E., Yen, J. et al. Leader identification in an online health community for cancer survivors: a social network-based classification approach. Inf Syst E-Bus Manage 13, 629–645 (2015). https://doi.org/10.1007/s10257-014-0260-5

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  • DOI: https://doi.org/10.1007/s10257-014-0260-5

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