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Markov enhanced graph attention network for spammer detection in online social network

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Abstract

Online social networks (OSNs) are an indispensable part of social communication where people connect and share information. Spammers and other malicious actors use the OSN’s power to propagate spam content. In an OSN with mutual relations between nodes, two kinds of spammer detection methods can be employed: feature based and propagation based. However, both of these are incomplete in themselves. The feature-based methods cannot exploit mutual connections between nodes, and propagation-based methods cannot utilize the rich discriminating node features. We propose a hybrid model—Markov enhanced graph attention network (MEGAT)—using graph attention networks (GAT) and pairwise Markov random fields (pMRF) for the spammer detection task. It efficiently utilizes node features as well as propagation information. We experiment our GAT model with a smoother Swish activation function having non-monotonic derivatives, instead of the leakyReLU function. The experiments performed on a real-world Twitter Social Honeypot (TwitterSH) benchmark dataset and subsequent comparative analysis reveal that our proposed MEGAT model outperforms the state-of-the-art models in accuracy, precision–recall area under curve (PRAUC), and F1-score performance measures.

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Notes

  1. https://datareportal.com/reports/digital-2022-global-overview-report.

  2. https://github.com/dleyan/MDGCN/tree/master/MDGCN/datasets/TwitterSH/data.

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Author notes

  1. Ashutosh Tripathi, Mohona Ghosh and Kusum Kumari Bharti have contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751020, India

    Ashutosh Tripathi

  2. Information Technology, Indira Gandhi Delhi Technical University for Women, New Delhi, Delhi, 110006, India

    Mohona Ghosh

  3. Information Technology, Dr. B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India

    Kusum Kumari Bharti

Authors
  1. Ashutosh Tripathi
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  2. Mohona Ghosh
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  3. Kusum Kumari Bharti
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Contributions

AT conceptualized the research and designed the study. AT also performed data analysis and drafted the initial manuscript. MG contributed to the study design and provided critical input during data analysis and interpretation. MG also played a significant role in manuscript writing, editing, and revising. KKB contributed to literature review, study design and manuscript preparation. KKB also provided valuable insights into the discussions of the results section. All authors reviewed and approved the final version of the manuscript, ensuring its accuracy and scientific rigor. Each author has read and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Ashutosh Tripathi, Mohona Ghosh or Kusum Kumari Bharti.

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Tripathi, A., Ghosh, M. & Bharti, K.K. Markov enhanced graph attention network for spammer detection in online social network. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02137-z

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