Advancing Fake News Detection: A Comparative Analysis of SVM and LR Models and Prospects for Dynamic Model Updating

Fake news has grown to be a significant issue in the present digital era, affecting both public opinion and information accuracy. Creating effective detection models that can distinguish between fake and real information is essential to getting beyond that obstacle. To identify fake news, we conducted an analysis of two well-known ML techniques, Support Vector Machine (SVM) and Logistic Regression (LR), in this research. We used an extensive data set having real news stories and their fake equivalents. By means of preprocessing methods including tokenization, stemming, and feature engineering, we derive meaningful information from the raw material. We next used the processed dataset to train SVM and LR classifiers, using different configurations for the detection of fake news. Evaluation measures that calculate the models' efficiency in differentiating between fake and real news include precision, F1-score, recall, and accuracy. Our test results demonstrate the effectiveness of both SVM and LR in identifying fake news, with SVM demonstrating somewhat superior accuracy rates in several cases. LR, however, performs competitively, especially where interpretability and computational efficiency are paramount. We intend to present a new approach to dynamic model updating in the future, in which real time data will be routinely and methodically added to the detection model. By enhancing the capacity of the model to recognize new types of disinformation, this adaptive approach seeks to increase the model's overall relevance and accuracy in real-time identification of fake news.