In an era where information spreads at lightning speed, it’s crucial to have tools that can help us discern fact from fiction. This is where fake news detection comes in.
In this project, we’ll explore the power of machine learning to combat the spread of misinformation. We’ll build a model that can effectively classify news articles as real or fake, and we’ll even incorporate fact-checking capabilities to verify claims made within those articles.
Here’s a breakdown of the key steps involved:
1. Data Collection and Preparation
- First I gathered a diverse dataset of news articles, labeled as either real or fake.
- Next, I preprocessed the text data to extract meaningful features, such as word frequencies, sentiment scores, and writing style characteristics.
2. Model Training
- In second step, I selected a suitable machine learning algorithm for classification, the Passive Aggressive Classifier.
- Then I trained the model on the prepared dataset, allowing it to learn the patterns that distinguish real news from fake news.
3. Fact-Checking Integration
- In third step, I utilized Python libraries to automate the process of extracting claims from articles.
- Then used a CSV File to access data of known facts or reputable sources to verify the accuracy of these claims.
- I also incorporated fact-checking results into the model’s prediction process to enhance its credibility assessment.
4. Model Evaluation
- In fourth step, I rigorously tested the model’s performance on unseen data to assess its accuracy and ability to generalize.
- I also utilized metrics like precision, recall, and F1-score to evaluate its effectiveness.
5. GUI Integration
- Used Kivy as it is lightweight and cross-platform making it accessible for all users.
- It has text area for user input, a button to trigger the prediction, and another text area to display the prediction result.