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A comprehensive review on automatic detection of fake news on social media

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Abstract

Social media sites are now quite popular among internet users for sharing news and opinions. This has become possible due to the inexpensive Internet, easy availability of digital devices, and the no-cost policy to create a user account on social media platforms. People are fascinated by social media sites because they can easily connect with others to share their interests, news, and opinions. According to studies, someone who lacks credibility is more likely to spread false information in order to achieve goals of any kind, be it influencing political opinions, earning attention, or making money. The automatic detection of social media related fake news has thus emerged as a highly anticipated research area in recent years. This paper offers a comprehensive review of the automatic detection of fake news on social media platforms. It contains details of the key models or techniques related to machine/deep learning proposed (or developed) during the period of the year 2011 to the year 2022 along with the performance metrics of each model or technique. The paper discusses (a). the key challenges faced during the development of an effective and efficient fake news detection system, (b). some popular datasets for carrying out fake news detection related research, and (c). the major research gaps, and future research directions in the area of automatic fake news detection on social media.

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Data Availability

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

Notes

  1. https://www.scopus.com

  2. https://www.kaggle.com/general/232077

  3. https://paperswithcode.com/dataset/liar

  4. https://www.kaggle.com/datasets/mdepak/fakenewsnet

  5. https://github.com/thiagovas/bs-detector-dataset/

  6. https://www.kaggle.com/general/35739

  7. https://github.com/BuzzFeedNews/

  8. https://github.com/ww-rm/weibo-rmdt

  9. https://www.kaggle.com/datasets/usharengaraju/pheme-dataset

  10. https://github.com/compsocial/CREDBANK-data

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Authors and Affiliations

  1. Department of Computer Science Engineering, School of Engineering Science & Technology, Jamia Hamdard University, New Delhi, 110062, India

    Manish Kumar Singh, Jawed Ahmed & Mohammad Afshar Alam

  2. Department of Computer Science, Ramanujan College, University of Delhi, Kalkaji, New Delhi, 110019, Delhi, India

    Kamlesh Kumar Raghuvanshi

  3. Cluster Innovation Center, University of Delhi, GC Narang Road, New Delhi, 110007, Delhi, India

    Sachin Kumar

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Singh, M.K., Ahmed, J., Alam, M.A. et al. A comprehensive review on automatic detection of fake news on social media. Multimed Tools Appl 83, 47319–47352 (2024). https://doi.org/10.1007/s11042-023-17377-4

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