Fake News Detection Using Improved Machine Learning Approaches

The content-based text features machine learning method of detecting fake news is highly useful. It consists of effective text preprocessing. Next, the textual data are converted to numeric information employing Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. The classic automated approaches, such as the Random Forest (RF) and the Gradient enhancer based, were employed and demonstrated a great level of accuracy in identifying fake and real news. The algorithm is computationally efficient and scalable. The study manly focuses on the significance of descriptors algorithms and classic models in obtaining reliable results without the high computational overhead of advanced high dimension data-based approaches. Nevertheless, additional approaches, including BERT and Long Short-Term Memory (LSTM), and social graph-based features can be incorporated into future research to increase its accuracy and generalizability and obtain relational and contextual cues.