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Identifying social roles using heterogeneous features in online social networks

Author

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  • Yezheng Liu
  • Fei Du
  • Jianshan Sun
  • Thushari Silva
  • Yuanchun Jiang
  • Tingting Zhu

Abstract

Role analysis plays an important role when exploring social media and knowledge‐sharing platforms for designing marking strategies. However, current methods in role analysis have overlooked content generated by users (e.g., posts) in social media and hence focus more on user behavior analysis. The user‐generated content is very important for characterizing users. In this paper, we propose a novel method which integrates both user behavior and posted content by users to identify roles in online social networks. The proposed method models a role as a joint distribution of Gaussian distribution and multinomial distribution, which represent user behavioral feature and content feature respectively. The proposed method can be used to determine the number of roles concerned automatically. The experimental results show that the proposed method can be used to identify various roles more effectively and to get more insights on such characteristics.

Suggested Citation

  • Yezheng Liu & Fei Du & Jianshan Sun & Thushari Silva & Yuanchun Jiang & Tingting Zhu, 2019. "Identifying social roles using heterogeneous features in online social networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(7), pages 660-674, July.
    • Handle: RePEc:bla:jinfst:v:70:y:2019:i:7:p:660-674
    DOI: 10.1002/asi.24160
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    Cited by:

    1. Miyoung Chong & Hae Jung Maria Kim, 2020. "Social roles and structural signatures of top influentials in the #prayforparis Twitter network," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 315-333, February.

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