Article
SYSTEMATIC REVIEW ON FAKE NEWS & DISINFORMATION USING ML
The rapid growth of social media platforms has significantly transformed the way information is produced, shared, and consumed across the world. While these platforms provide a powerful medium for communication and information exchange, they also facilitate the rapid spread of fake news and disinformation. Such misinformation can influence public opinion, disrupt political processes, and create social instability. Traditional fact-checking mechanisms rely heavily on manual verification, which is time-consuming, resource-intensive, and incapable of handling the massive volume of online content generated daily. Consequently, automated approaches using Machine Learning (ML) and Natural Language Processing (NLP) have emerged as promising solutions for identifying and mitigating fake news. This study presents a systematic review and comparative analysis of machine learning and deep learning techniques used for fake news detection. In particular, the research focuses on the performance comparison between Random Forest, a widely used machine learning algorithm, and Long Short-Term Memory (LSTM), a deep learning architecture designed for sequential text processing. Random Forest utilizes textual features extracted through TF-IDF vectorization, while LSTM leverages word embeddings to capture contextual semantics and long-term dependencies within textual data. The proposed framework includes data collection from benchmark datasets, text preprocessing, feature extraction, model training, and evaluation using performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that deep learning models capture contextual patterns more effectively, whereas traditional machine learning models provide faster training and interpretability. The study highlights the importance of combining linguistic features with deep contextual learning to improve misinformation detection. Furthermore, it identifies research gaps and future opportunities, including multilingual detection systems, transformer-based models, and real-time deployment for social media monitoring
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