With the proliferation of misinformation online, automated fake news detection has become crucial. This project tackles this challenge using machine learning to classify news articles as either real or fake. The objective was to identify the most effective algorithm for this task, so I implemented and tested three models: Naive Bayes, Decision Tree, and Logistic Regression.
The process began by gathering a labeled dataset of real and fake news articles. After extensive data preprocessing, text-based features were extracted using TF-IDF (Term Frequency-Inverse Document Frequency) to transform text into numerical vectors suitable for model training. Each model was trained on this dataset and evaluated on a separate test set.
The Naive Bayes algorithm was selected for its simplicity and efficiency in handling text classification. The Decision Tree algorithm offered interpretability, making it easier to understand the model’s decision-making process. Logistic Regression, a strong choice for high-dimensional text data, provided robust performance and proved to be the most accurate in distinguishing real news from fake news.
Each algorithm’s performance was assessed using accuracy, precision, recall, and F1-score. Decision Tree achieved the highest accuracy, followed by Logistic Regression and Naive Bayes. The findings revealed:
- Logistic Regression: Demonstrated superior predictive power and adaptability, making it a good fit for high-stakes scenarios where accuracy is crucial.
- Decision Tree: Offered strong interpretability, making it useful in contexts requiring transparency, with high overall accuracy.
- Naive Bayes: Efficient and fast, suitable for real-time applications but less accurate than the other models.
The results show the potential of machine learning to automate the detection of fake news and reduce the spread of misinformation. However, limitations like data quality dependence and evolving fake news patterns suggest that future improvements could involve integrating ensemble methods (e.g., Random Forest, XGBoost) or deep learning techniques (e.g., CNN, LSTM) to capture more nuanced language patterns and boost accuracy further. I plan to explore additional features, such as sentiment analysis and metadata, to refine the detection capabilities and enhance model robustness.
This project showcases my expertise in machine learning, text processing, and model evaluation, as well as my commitment to addressing impactful issues through data science.