Enhanced Detection of Misinformation Text-Based Fake News Analysis

In today's digitally-driven society, fake news has become a common problem, spreading disinformation and having unfavorable effects. Addressing this issue poses several challenges, including the detection of nuanced language patterns, handling the large volume and variety of news content, and distinguishing between satire, misinformation, and genuine news. Recent techniques using machine learning have shown promise but often struggle with high false positive rates and scalability issues. This research work proposes a machine learning-based method for identifying false news that combines advanced feature selection, classification algorithms, and natural language processing approaches. The proposed method examines the textual content of news items and extracts pertinent characteristics such as sentiment analysis, metadata, source reliability, and language patterns. The proposed system uses feature selection techniques to identify the most informative features and reduce the dataset's dimensionality. Then, this study train and evaluate the model using a hybrid stacking classifier that integrates Random Forest, XGBoost, and Logistic regression, achieving high recall rates and precision. The proposed approach addresses the challenges of existing systems by improving accuracy and scalability, effectively recognizing and highlighting potentially misleading or inaccurate material to combat fake news.