Application of Artificial Intelligence Techniques to Detect Fake News: A Review
Penerapan Teknik Kecerdasan Buatan untuk Mendeteksi Berita Palsu: Sebuah Tinjauan
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(1) * Muthnna Nihad Al-Tameemi
Iraq
(*) Corresponding Author
Abstract
General Background: The rapid expansion of social media platforms and online news has led to an increase in fake news, impacting the accuracy and reliability of disseminated information. Specific Background: As machine learning excels at handling large datasets and pattern recognition, it has become a prominent tool in fake news detection by identifying signs of misinformation through textual content, social context, and network structures. Knowledge Gap: Although recent advances exist, challenges persist in handling different data types and optimizing model performance, as well as ensuring accuracy across languages and platforms. Aims: This article aims to review the latest advancements in fake news detection through machine learning, focusing on various techniques like data mining, deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based approaches. Results: The findings show that each technique has specific advantages and limitations; for example, deep learning uncovers complex linguistic features, while ensemble learning increases accuracy through combined models, and graph-based techniques leverage social network analysis but lack linguistic context. Novelty: The review identifies emerging trends, including hybrid models that combine NLP, graph-based, and deep learning approaches for broader, multi-perspective detection, as well as optimization techniques using precomputed data and pre-trained algorithms. Implications: The discussion highlights the potential for these combined approaches to enhance fake news detection accuracy and efficiency on social media, suggesting that integrating these methods with quantum computing advancements in NLP could further improve model performance. In summary, the article provides a comprehensive analysis of the machine learning landscape in fake news detection and recommends future directions for developing more scalable, effective solutions.
Highlights:
- Scalable machine learning aids in fake news detection on social media.
- Hybrid NLP and graph models improve detection accuracy.
- Optimized pre-trained models enhance efficiency and performance.
Keywords: Fake News Detection, Machine Learning, Social Media, Natural Language Processing, Graph-Based Techniques
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