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<p>Detecting fake news on social media platforms such as Facebook, Twitter, and Quora has become a critical concern for researchers. Motivations for spreading false information range from political and social agendas to deliberate attempts to disrupt national or global stability. With advancements in technology and artificial intelligence, fake news increasingly incorporates multimodal content such as text, images, audio, and video, making it more difficult to distinguish from truthful information. Addressing this challenge is essential for effective misinformation detection, raising public awareness, and maintaining social stability. This review examines the key challenges, concerns, and solutions, with a focus on machine learning and deep learning approaches for detecting multimodal fake news on social media. The article also provides details and links to publicly available datasets that may benefit students and researchers interested in exploring this domain. In addition, a bibliometric analysis is presented to highlight research trends and the significance of this topic within the scientific community.</p>

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Toward efficient multimodal fake news detection: issues, methods, and directions

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • Anna Mikayelyan,
  • Amalya Tadevosyan,
  • Sachin Kumar

摘要

Detecting fake news on social media platforms such as Facebook, Twitter, and Quora has become a critical concern for researchers. Motivations for spreading false information range from political and social agendas to deliberate attempts to disrupt national or global stability. With advancements in technology and artificial intelligence, fake news increasingly incorporates multimodal content such as text, images, audio, and video, making it more difficult to distinguish from truthful information. Addressing this challenge is essential for effective misinformation detection, raising public awareness, and maintaining social stability. This review examines the key challenges, concerns, and solutions, with a focus on machine learning and deep learning approaches for detecting multimodal fake news on social media. The article also provides details and links to publicly available datasets that may benefit students and researchers interested in exploring this domain. In addition, a bibliometric analysis is presented to highlight research trends and the significance of this topic within the scientific community.