How can we identify biases & nonfactual information in the news around us? More specifically, how do we compare the stance of a given claim with that of reputable news sources across the political spectrum/grid? In this today’s connected world where anyone, anywhere, of virtually any background can become their own reporter, it is imperative that readers have a way to understand the validity of the news they read & potential for bias.
The final goal of this project is to develop a system for identifying fake news based on bias (or stance), knowledge, style, and propagation, and deploy this model in a way that is easily accessible by a user. The team will work on creating the Chrome extension that can predict whether a claim (1-2 sentences) is true and provide relevant articles from multiple perspectives on the issue. Users can highlight any claim on any website, and our tool will automatically run a model to predict how likely a claim is to be fake based on other articles’ reputation, stance, and bias on an issue.
Project Lead: Eshani Chauk
Platform: Anthony Xue, Yash Gupta, Aditi Prakash
Analysis: Aayush Mathur, Utkarsh Nattamai Subramanian Rajkumar, Prabhanjan Nayak
Data viz: Justin Huang, Ayush Baweja, Akash Vemulapalli