Developed a browser extension to highlight fake news and political bias that won at the HackUCI hackathon


Fake news and the spread of biased misinformation is one of the most prevalent social problems in American and global society. Pew Research Center found 68% of Americans say fake news undermines their confidence in government institutions and 56% believe the problem will only get worse over time. Pew also found that heavily biased news is accelerating polarization as the more clickbait or eye-catching a headline is, the more views it attracts. To make matters worse, according to a Stanford study, Americans fail to identify fake news over real news with a “stunning and dismaying consistency.” This can create confusion and misunderstanding. The more misinformed people are, the less society is able to solve important social and political issues. Misinformation can also be weaponized, as seen with current Russian propaganda amid their invasion of Ukraine.


Biascope empowers users to be active consumers of news. We help people break free from the harmful echo chambers of biased news by intuitively displaying the bias and accuracy of news articles without extra effort from the consumer. By color-highlighting bias in articles and displaying metrics on news sites, our extension trains users to notice biased language and how it influences their interpretation of content. Promoting thought around news rather than accepting what’s given at face value combats the spread of misinformation.

How we built it

We built a FastAPI Python backend with a Postgres database, and a Firefox/Chrome browser extension. Originally, we were going for a web app so we used this react-fastapi template, but we realized after some brainstorming that we really only need a browser extension to have an effective product. We used Poetry for python dependency management and Docker images to handle running the frontend-backend stack.

Challenges we ran into

Training the AI model took far longer than we anticipated, though helped with this process. Building our training dataset was also time consuming.

Accomplishments that we're proud of

We built a bias-detecting transformer model with 73% accuracy! And built a neat browser extension to display the data in an intuitive way :)

Tech Stack
Frontend: Cross-platform browser extension (TypeScript, JavaScript)
Backend: FastAPI + Postgres (Python, SQL)
ML: Tensorflow, scikit-learn, mageAI