A Complete Implementation of Fake News Detection System Based on Machine Learning and NLP: From Text Cleaning to Multi-Model Comparison

This article introduces an open-source fake news detection project, covering data preprocessing, feature extraction (Bag-of-Words and TF-IDF), and comparative experiments of four classic machine learning algorithms: Naive Bayes, Logistic Regression, SVM, and Random Forest, providing reproducible technical references for text classification tasks.

In the era of information explosion, fake news spreads much faster than real information, posing severe challenges to social stability, public health, and even democratic elections. Traditional manual review can hardly meet the demand for massive content, making automated fake news detection a hot topic. Fake news detection is essentially a binary classification task (real marked as 1/fake marked as 0), but fake news often imitates real styles and contains partial real information, requiring capture of deep semantic and stylistic differences.

Adopting a classic machine learning pipeline architecture: Data Preprocessing → Feature Engineering → Model Training → Performance Evaluation. Data preprocessing includes removing HTML tags, converting to lowercase, removing punctuation and numbers, word segmentation, and stopword filtering; feature engineering implements two text representation methods: Bag-of-Words (vector of word occurrence counts) and TF-IDF (term frequency + inverse document frequency weights).

Comparing four classic algorithms:

1. Naive Bayes: Based on Bayes' theorem, assumes feature independence, efficient computation, suitable for high-dimensional sparse text;
2. Logistic Regression: Maps to probability via sigmoid function, strong interpretability, fast training speed;
3. SVM: Finds the optimal hyperplane to maximize class margin, linear SVM performs well in text classification;
4. Random Forest: Integrates multiple decision trees, anti-overfitting, robust to noisy data.

Using four evaluation metrics:

• Accuracy: The proportion of correctly predicted samples to total samples;
• Precision: The proportion of true positives among predicted positives;
• Recall: The proportion of true positives correctly predicted;
• F1 Score: Harmonic mean of precision and recall. Fake news detection needs to balance precision and recall, and F1 provides a balanced perspective.

Practical Insights:

1. Preprocessing quality directly affects performance and needs to be designed according to task characteristics;
2. TF-IDF is better than Bag-of-Words, but cannot capture word order and semantics; try word embedding or pre-trained models;
3. Model selection depends on needs: use deep learning for accuracy, choose Logistic Regression/Naive Bayes for fast deployment. Future Directions: Introduce deep learning (LSTM/BERT) to capture semantics, combine multi-modal information, build knowledge graphs to verify facts, and develop interpretable models.