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Proceedings of the Future Technologies Conference

FTC 2018: Proceedings of the Future Technologies Conference (FTC) 2018 pp 343–358Cite as

Anomaly Detection in Q & A Based Social Networks

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 880))

Abstract

Detection of anomalies in question/answer based social networks is important in terms of finding the best answers and removing unrelated posts. These networks are usually based on users’ posts and comments, and the best answer is selected based on the ratings by the users. The problem with the scoring systems is that users might collude in rating unrelated posts or boost their reputation. Also, some malicious users might spam the discussion. In this paper, we propose a network analysis method based on network structure and node property for exploring and detecting these anomalies.

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Author information

Authors and Affiliations

  1. Computer and IT Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Neda Soltani & S. Alireza Hashemi Golpayegani

  2. Computer and IT Engineering Department, University of Science and Technology, Babol, Mazandaran, Iran

    Elham Hormizi

Authors
  1. Neda Soltani
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  2. Elham Hormizi
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  3. S. Alireza Hashemi Golpayegani
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Corresponding author

Correspondence to Neda Soltani .

Editor information

Editors and Affiliations

  1. Saga University , Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

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© 2019 Springer Nature Switzerland AG

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Soltani, N., Hormizi, E., Golpayegani, S.A.H. (2019). Anomaly Detection in Q & A Based Social Networks. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_27

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