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User characterization for online social networks

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Abstract

Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender and age; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots versus humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights into researchers and better services to online users.

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Authors and Affiliations

  1. Department of Computer Science, University of Houston, Houston, TX, 77204-3010, USA

    Tayfun Tuna

  2. Department of Computer Science, Florida State University, Tallahassee, FL, 32306-4530, USA

    Esra Akbas

  3. Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, 89557, USA

    Ahmet Aksoy & Muhammed Abdullah Canbaz

  4. Department of Computer Science, Sam Houston State University, Huntsville, TX, 77341, USA

    Umit Karabiyik

  5. School of Information Technology, University of Cincinnati, Cincinnati, OH, 45221, USA

    Bilal Gonen

  6. Department of Computer Science, University of Alabama in Huntsville, Huntsville, AL, 35899, USA

    Ramazan Aygun

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  1. Tayfun Tuna
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  2. Esra Akbas
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Correspondence to Tayfun Tuna.

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Tuna, T., Akbas, E., Aksoy, A. et al. User characterization for online social networks. Soc. Netw. Anal. Min. 6, 104 (2016). https://doi.org/10.1007/s13278-016-0412-3

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