Bubble Terminator

Inspiration In the age of personalized searches and algorithmic recommendation, the filter bubble is increasingly becoming a social and political problem. The term filter bubble, coined by Internet activist Eli Pariser, refers to a state of intellectual isolation as a result of a recommendation algorithm selectively guesses what content a user would like to see based on information about the user, such as location, past click-behavior and search history. In consequence, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles(To learn more about filter bubble, please refer to this link).

“Contrasting views also deserve to be recommended in such cases”. With this idea, we designed this clustering and recommendation system with completely different recommendation principles. In contrast to traditional recommendation algorithm which regards entertaining and engaging users as the first principle, Bubble Terminator would help users understand the position he/she stands in the big picture of a problem/topic and brings users a brand new perspective, aiming to encourage having deeper awareness of a problem and exchange of different ideologies.

What it does Real-time analysis of a text input (twitters) about its political biases (could be extended to more dimensions in the future) and reflect the prediction result. Display warning when a political bias is detected in the current twitter page (To be developed) Real-time analysis on a Twitter user’s political leaning. (To be developed) Recommend contrasting views of a topic to users. How we built it There are two parts to this project:

CHROME EXTENSION The chrome extension is written in Javascript, using libraries such as React and Ant Design. We use content script to read and make changes to a twitter page.