Fake News Detector

AI tool for identifying fake news in climate change reports.

The Fake News Detector is a machine learning-based application designed to identify and classify news related to climate change as real or fake. It employs advanced natural language processing techniques and deep learning models to distinguish factual news from fabricated stories, especially in the sensitive domain of climate change.

Role and Team Collaboration:

In this project, I played a key role in developing the application's architecture, implementing the machine learning models, and integrating the Flask API. I collaborated with a team of data scientists and software developers to ensure the success of the project.

Outcome:

The project involved scraping news data from various sources and processing it using text analysis and Optical Character Recognition (OCR) features. Text classification models were employed to analyze and classify the news content as real or fake. The application offers a Flask API for easy interaction with the model.

Challenges and Solutions:

One of the main challenges we faced was gathering and processing reliable datasets for training the model. To address this, we developed scraping tools to collect news data from specified sources, ensuring a robust and diverse dataset for model training.

Impact and Applications:

The Fake News Detector can be used by journalists and media houses to verify the authenticity of news, particularly in the domain of climate change. It also helps in raising public awareness about misinformation in climate change news.

Conclusion and Learnings:

The Fake News Detector represents a significant step towards leveraging AI in combating misinformation. This project emphasized the importance of accurate data and robust natural language processing models in fake news detection.

Artifacts Created:

• Repository Link: Fake News Detector on GitHub