Daniel Miao¹, Tyler Liu¹ and Andrew Park², ¹USA, ²California State Polytechnic University, USA

The advent and spread of the internet has caused many users to favor the convenience and breadth of reporting that online news offers, whether it be from media companies or social platforms, which in turn has led to the monetization and corruption of said stories [4]. Large, company-owned news sites each try to appeal to only a few groups across the political spectrum, oftentimes sacrificing the indifference and integrity which serve as the tenets of honest journalism. We propose to aid in solving this problem a Chrome extension which serves to provide metrics, information, and line-by-line analysis of article text in order to help readers stay aware and healthily skeptical [5][6]. Using machine learning (ML) as well as traditional algorithms, we aim to provide key info on the article's truthfulness as well as the source's bias and ownership [7]. In this project, we used 3 main models, each to detect fake news, political leaning and sentiment, in addition to traditional criteria such as readability, # of words, and time to read. All of our models performed well both theoretically and practically, giving above 80% accuracy on all occasions.

Artificial Intelligence, Fake News, Chrome Extension, Text Analysis