Using ML to detect suspicious news articles
Fake news is prevalent on online news sites, and it is increasingly difficult to distinguish credible information from articles meant to spread misinformation. Often, this requires doing extensive research and cross-checking of sources, which takes an immense amount of time and effort.
Leveraging big data and today's massive computational power, we tailored our model to 10 million news articles from over 1000 different online news websites in order to classify news articles as completely fake, largely political, or credible.
After iterating on network design and training for multiple days, our model can now correctly categorize news articles 94% of the time, a result comparable to that of existing fact-checking watchdogs, but our fully-automated solution lets users know whether or not to trust articles with a single click.