Fake news is a growing problem in today's digital world. Misinformation spreads quickly, leading to social and political consequences. Using machine learning, we can automatically classify news articles as real or fake, helping people verify information easily

1. Fake News Detection Using
Machine Learning
• Presented by: [Your Name]
• Institution: [Your Institution]

2. Introduction
• • Fake news spreads misinformation and
influences public opinion.
• • Social media and online platforms make it
easy to distribute false information.
• • Need for automated detection using
Machine Learning.

3. Problem Statement
• • Traditional fact-checking is slow and manual.
• • Fake news is designed to look credible.
• • Challenge: How to automatically detect fake
news efficiently?

4. Objective
• • Develop a Machine Learning model to
classify news as real or fake.
• • Use Natural Language Processing (NLP)
techniques.
• • Improve accuracy and reliability of fake news
detection.

5. Existing Solutions
• • Manual fact-checking (slow and subjective).
• • Keyword-based detection (not always
accurate).
• • Need for AI-based solutions for better
accuracy.

6. Proposed Solution
• • Use NLP and ML algorithms to analyze text
patterns.
• • Train models on labeled datasets.
• • Automate detection with higher accuracy.

7. Methodology
• • Dataset: Collect news articles labeled as real
or fake.
• • Feature Extraction: TF-IDF, Word
Embeddings.
• • ML Models: Logistic Regression, Random
Forest, LSTM, BERT.
• • Model Training and Evaluation.

8. Implementation
• 1. Data Collection & Preprocessing.
• 2. Feature Engineering.
• 3. Model Selection & Training.
• 4. Testing & Evaluation.
• 5. Deployment (Optional).

9. Expected Results
• • Improved accuracy in detecting fake news.
• • Faster and automated classification.
• • Comparison with traditional methods.

10. Challenges & Future Scope
• • Handling evolving fake news patterns.
• • Dataset biases and accuracy issues.
• • Future: Real-time detection, multi-language
support, deep learning improvements.

11. Conclusion
• • ML can enhance fake news detection.
• • Helps in minimizing misinformation spread.
• • Future improvements for real-time detection
systems.

12. Q&A
• Thank you!
• Questions?