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@article{197208,
        author = {Shrey S. Shah and Jaykumar A. Patel and Krishna Joshi},
        title = {MACHINE LEARNING-BASED DETECTION OF FAKE NEWS AND STOCK MARKET MANIPULATION: A SYSTEMATIC REVIEW},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6073-6079},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197208},
        abstract = {The spread of counterfeit news on the internet is increasingly becoming a significant risk to the integrity of the financial markets. Planned misinformation of equity, cryptocurrency, and commodity markets create unnatural price fluctuations and distort the trading volumes. This is a systematic review that brings together 30 peer-reviewed articles published in 2020-25 on the use of machine learning (ML) and natural language processing (NLP) in detecting fake news and identifying the presence of manipulated stocks on the stock market. In line with PRISMA standards, we answer three research questions: what are the most effective ML/NLP architectures to detect financial fake news; what market microstructure features go hand in hand with manipulation; and what knowledge gaps can be found in cross-modal and explainability-oriented methods. Transformer-based models, in particular, BERT and FinBERT, achieve better results than classical baselines of ML. The best detection performance is achieved with cross-modes that use signals, such as text, in conjunction with market data. Explainability, adversarial robustness, multilingual coverage, and real-time deployment are critical gaps that still exist.},
        keywords = {fake news detection, financial misinformation, machine learning, market manipulation, natural language processing, systematic review, transformer models},
        month = {April},
        }