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Toward rumor detection in social networks using multi-layer autoencoder neural network

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A Correction to this article was published on 17 January 2024

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Abstract

In recent years, the issue of spreading rumors as a big challenge in social networks has attracted a lot of attention. The spread of rumors on social networks is remarkably fast, and using manual methods to detect them is inefficient. Therefore, this paper focuses on the automatic detection of rumors in social networks. Recent automatic rumor detection models have some limitations and do not make sufficient use of available information. Here, we use a wide range of information such as surface features, content of posts, contexts of replies, and relationships between components to automatically detect rumors. Meanwhile, we propose a multi-layer autoencoder neural network to automatically detect rumors in social networks. Here, the autoencoder model is configured with several innovative thresholds for rumor detection to provide better performance than existing models. We carried out extensive experiments on real social networks such as Sina Weibo to prove the rationality of the proposed rumor detection model. Numerical simulations show that the accuracy of our model, including early detection, has achieved improvements compared to existing works.

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Data sharing is not applicable to this manuscript as no datasets were generated or analyzed during the current study.

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Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

    Amin Qourbani

  2. Department of Human Resources Studies and Evaluation, Research Center of Knowledge-Based Businesses and Resource Management Studies, Tehran, Iran

    Mahdi Khodaparast

  3. Department of Computer Science, College of Science, Cihan University-Erbil, Erbil, Iraq

    Rebaz Othman Yahya

  4. Faculty of Technical Engineering, Malayer University, Malayer, Iran

    Mohammadhossein Habibi

  5. Sub-Department of Operations and Analytics, Department of Management, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Atieh Nouralishahi

  6. Department of Computer Engineering, Persian Gulf University, Bushehr, Iran

    Amin Rezaeipanah

Authors
  1. Amin Qourbani
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  2. Mahdi Khodaparast
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  3. Rebaz Othman Yahya
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  4. Mohammadhossein Habibi
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  5. Atieh Nouralishahi
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  6. Amin Rezaeipanah
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Contributions

All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

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Correspondence to Amin Qourbani or Amin Rezaeipanah.

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Qourbani, A., Khodaparast, M., Othman Yahya, R. et al. Toward rumor detection in social networks using multi-layer autoencoder neural network. Soc. Netw. Anal. Min. 14, 8 (2024). https://doi.org/10.1007/s13278-023-01170-0

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