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Joint inference of user community and interest patterns in social interaction networks

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Abstract

Online social media have become an integral part of our social beings. Analyzing conversations in social media platforms can lead to complex probabilistic models to understand social interaction networks. In this paper, we present a modeling approach for characterizing social interaction networks by jointly inferring user communities and interests based on social media interactions. We present several pattern inference models: (1) interest pattern model (IPM) captures population level interaction topics, (2) user interest pattern model (UIPM) captures user specific interaction topics, and (3) community interest pattern model (CIPM) captures both community structures and user interests. We test our methods on Twitter data collected from Purdue University community. From our model results, we observe the interaction topics and communities related to two big events within Purdue University community, namely Purdue Day of Giving and Senator Bernie Sanders’ visit to Purdue University as part of Indiana Primary Election 2016. Constructing social interaction networks based on user interactions accounts for the similarity of users’ interactions on various topics of interest and indicates their community belonging further beyond connectivity. We observed that the degree-distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interaction. We also discuss the application of such networks as a useful tool to effectively disseminate specific information to the target audience towards planning any large-scale events and demonstrate how to single out specific nodes in a given community by running network algorithms.

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Acknowledgements

The authors are grateful to National Science Foundation for the grant CMMI-1131503 and CMMI-1520338 to support the research presented in this paper. However, the authors are solely responsible for the findings presented in this study.

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Authors and Affiliations

  1. Moss School of Construction, Infrastructure and Sustainability, Florida International University, 10555 West Flagler Street, Miami, FL, 33174, USA

    Arif Mohaimin Sadri

  2. Department of Civil, Environmental, and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Orlando, FL, 32816, USA

    Samiul Hasan

  3. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Satish V. Ukkusuri

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  1. Arif Mohaimin Sadri
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Sadri, A.M., Hasan, S. & Ukkusuri, S.V. Joint inference of user community and interest patterns in social interaction networks. Soc. Netw. Anal. Min. 9, 11 (2019). https://doi.org/10.1007/s13278-019-0551-4

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