Fact Check

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  FactCheck

Inspiration

In a time where misinformation spreads faster than truth, we felt a need for change. Our system empowers people to verify facts and reclaim trust in digital news.

What it does

It analyzes news content using natural language processing to detect misleading or false information. The system flags suspicious articles, helping users identify and avoid fake news.

How we built it

We trained a machine learning model using real and fake news datasets, applying NLP techniques for text analysis. The system was integrated into a web application using Flask and Python for real-time news verification.

Challenges we ran into

Distinguishing subtle differences between real and cleverly disguised fake news was difficult. Gathering and preprocessing diverse, high-quality datasets took significant time and effort.

Accomplishments that we're proud of

We developed a reliable AI model that accurately detects fake news with high precision. Our web app provides users with instant, easy-to-understand credibility checks, empowering informed decisions.

What we learned

We gained hands-on experience in natural language processing and machine learning model development. We also learned how to integrate AI models into user-friendly web applications for real-world use.

What's next for Fact Guard

We plan to enhance FactGuard with multi-language support and real-time social media monitoring. Future updates will include user feedback integration to continuously improve detection accuracy.

Built With

• aws-s3-other-tools:-git
• fact-checking-apis-(optional)-cloud-platforms:-heroku
• huggingface-transformers-databases:-mongodb-apis:-news-apis
• javascript-frameworks:-flask
• languages:-python
• react-ai/ml-libraries:-tensorflow
• render
• scikit-learn
• spacy