Lecture Notes: Building a Fake News Detection System

Step-by-Step Process

Step 1: Load Dataset

• Load datasets containing labeled real and fake news articles.

Step 2: Preprocess Data

• Clean the text data:
  • Remove punctuation.
  • Remove stopwords.
  • Convert text to lowercase.

Step 3: Tokenize Text

• Split the text into individual tokens (words).

Step 4: Vectorize Text

• Convert tokens into numerical representations:
  • Techniques such as TF-IDF or Word Embeddings are used.

Step 5: Train-Test Split

• Divide the dataset into training and testing sets:
  • Training set (e.g., 80%)
  • Testing set (e.g., 20%)

Step 6: Build Model

• Select an NLP classification model:
  • Options include Logistic Regression, Naive Bayes, or neural networks like LSTM/Transformer.

Step 7: Train Model

• Train the model using the training data to learn patterns indicative of real vs. fake news.

Step 8: Evaluate Model

• Test the model on the testing set:
  • Calculate metrics such as accuracy, precision, recall, and F1 score.

Step 9: Fine-Tune Model

• Optimize the model by adjusting hyperparameters:
  • Possible use of techniques like cross-validation.

Step 10: Deploy Model

• Save the trained model for deployment to predict new articles.

Step 11: Test on New Data

• Feed new articles to the model and output predictions (real/fake).

Detailed Steps

Step 1: Load Data

• Load datasets for real and fake news articles.

Step 2: Label Data

• Assign labels:
  • '1' for real news.
  • '0' for fake news.

Step 3: Combine Data

• Merge the datasets for processing.

Step 4: Preprocess Text

• Clean each article:
  • Lowercase conversion.
  • Punctuation removal.
  • Stopword removal.
  • Optional: Lemmatization or stemming.

Step 5: Convert Text to Numerical Format

• Apply techniques like TF-IDF to numerically represent the text.

Step 6: Split Data into Training and Testing Sets

• Training set for learning, testing set for evaluation.

Step 7: Select a Machine Learning Model

• Choose a model fitting for text classification tasks.

Step 8: Train the Model

• Model learns patterns between word usage and labels.

Step 9: Test the Model

• Evaluate the accuracy and performance using the test set.

Step 10: Fine-Tune (If Needed)

• Adjust model parameters to improve performance.

Step 11: Deploy the Model

• Save the model for real-time article classification.

Step 12: Make Predictions on New Articles

• Process new articles and predict labels:
  • '1' for likely real.
  • '0' for likely fake.

Output

• Predicted labels for new articles.