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Detecting Crowdsourcing Spammers in Community Question Answering Websites

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Book cover Advances in Internetworking, Data & Web Technologies (EIDWT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

Abstract

The growth of online crowdsourcing marketplaces has attracted massive normal buyers and micro workers, even campaigners and malicious users who post spamming jobs. Due to the significant role in information seeking and providing, CQA (Community Question Answering) has become a target of crowdsourcing spammers. In this paper, we aim to develop a solution to detect crowdsourcing spammers in CQA websites. Based on the ground-truth data, we conduct a hybrid analysis including both non-semantic and semantic analysis with a set of unique features (e.g., profile features, social network features, content features and linguistic features). With the help of proposed features, we develop a supervised machine learning solution for detecting crowdsourcing spammers in Community QA. Our method achieves a high performance with an AUC (area under the receiver-operating characteristic curve) value of 0.995 and an \(F_{1}\) score of 0.967, which significantly outperforms existing works.

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Authors and Affiliations

  1. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, China

    Kaiqing Hao & Lei Wang

Authors
  1. Kaiqing Hao
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  2. Lei Wang
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Corresponding author

Correspondence to Lei Wang .

Editor information

Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Hao, K., Wang, L. (2018). Detecting Crowdsourcing Spammers in Community Question Answering Websites. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_41

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

  • eBook Packages: EngineeringEngineering (R0)

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