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Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection

Author

Listed:
  • Long Song

    (City University of Hong Kong)

  • Raymond Yiu Keung Lau

    (City University of Hong Kong)

  • Ron Chi-Wai Kwok

    (City University of Hong Kong)

  • Kristijan Mirkovski

    (Victoria University of Wellington)

  • Wenyu Dou

    (City University of Hong Kong)

Abstract

With the rise of social web, there has also been a great concern about the quality of user-generated content on social media sites (SMSs). Deceptive comments harm users’ trust in online social media and cause financial loss to firms. Previous studies use various features and classification algorithms to detect and filter social spam on several social media platforms. However, to the best of our knowledge, previous studies have not exploited both probabilistic topic modeling and incremental learning to detect social spam on SMSs. Thus, the main contribution of this paper is design of a novel detection methodology that combines topic- and user-based features to improve the effectiveness of social spam detection. The proposed methodology exploits a probabilistic generative model, namely the labeled latent Dirichlet allocation (L-LDA), for mining the latent semantics from user-generated comments, and an incremental learning approach for tackling the changing feature space. An experiment based on a large dataset extracted from YouTube demonstrates the effectiveness of our proposed methodology, which achieves an average accuracy of 91.17 % in social spam detection. Our statistical analysis reveals that topic-based features significantly improve social spam detection, which has significant implications for business practice.

Suggested Citation

  • Long Song & Raymond Yiu Keung Lau & Ron Chi-Wai Kwok & Kristijan Mirkovski & Wenyu Dou, 2017. "Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection," Electronic Commerce Research, Springer, vol. 17(1), pages 51-81, March.
  • Handle: RePEc:spr:elcore:v:17:y:2017:i:1:d:10.1007_s10660-016-9244-5
    DOI: 10.1007/s10660-016-9244-5
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    References listed on IDEAS

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    1. Yindalon Aphinyanaphongs & Lawrence D. Fu & Zhiguo Li & Eric R. Peskin & Efstratios Efstathiadis & Constantin F. Aliferis & Alexander Statnikov, 2014. "A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(10), pages 1964-1987, October.
    2. Sara Owsley Sood & Elizabeth F. Churchill & Judd Antin, 2012. "Automatic identification of personal insults on social news sites," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(2), pages 270-285, February.
    3. Sara Owsley Sood & Elizabeth F. Churchill & Judd Antin, 2012. "Automatic identification of personal insults on social news sites," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(2), pages 270-285, February.
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    Cited by:

    1. Guo Li & Na Li, 2019. "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, Springer, vol. 19(4), pages 779-800, December.
    2. Martin Reisenbichler & Thomas Reutterer, 2019. "Topic modeling in marketing: recent advances and research opportunities," Journal of Business Economics, Springer, vol. 89(3), pages 327-356, April.
    3. Martin Kuchta & Linda Vaskova & Andrej Miklosik, 2020. "Facebook Explore Feed: Perception and Consequences of the Experiment," The Review of Socionetwork Strategies, Springer, vol. 14(1), pages 93-107, April.
    4. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.

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