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Detection of Fake News Using Deep Learning and Machine Learning

Author

Listed:
  • Gabriela CHIRIAC

    (The Bucharest University of Economic Studies, Romania)

  • Ada Maria CATINA

    (The Bucharest University of Economic Studies, Romania)

Abstract

Automatically identifying fake news is a complex challenge requiring detailed understanding of misinformation propagation and advanced data processing. Machine Learning and Deep Learning algorithms for detection demand continuous adaptation as disinformation tactics evolve. While promising, these technologies must be carefully calibrated for different contexts. This paper explores automated fake-news detection methods, analyzing their effectiveness and proposing improvements to address data quality, domain variability, and evolving disinformation strategies.

Suggested Citation

  • Gabriela CHIRIAC & Ada Maria CATINA, 2025. "Detection of Fake News Using Deep Learning and Machine Learning," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(1), pages 65-81.
    • Handle: RePEc:aes:dbjour:v:16:y:2025:i:1:p:65-81
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    References listed on IDEAS

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    1. Iftikhar Ahmad & Muhammad Yousaf & Suhail Yousaf & Muhammad Ovais Ahmad, 2020. "Fake News Detection Using Machine Learning Ensemble Methods," Complexity, Hindawi, vol. 2020, pages 1-11, October.
    2. Zhu, Hui & Wu, Heng & Cao, Jin & Fu, Gang & Li, Hui, 2018. "Information dissemination model for social media with constant updates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 469-482.
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