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IPFS News Link • Science, Medicine and Technology

A Data Scientist Was Sick of Seeing Spam on His Facebook so He Built a Fake News Detector


Tired of seeing his friends and family sharing questionable content on his Facebook feed, data scientist Zach Estela decided to take action. He built a tool that scans a website's most recent 100 posts and analyzes it to determine whether it's fake news, heavily biased, or a legit news source.

"I see my friends post, sometimes, complete garbage or articles recommended to me that are complete garbage," Estela told me over the phone.

As fake new purveyors have become more ubiquitous, they've also gotten more sophisticated. It's sometimes hard to tell if a news source is a small local paper, a Russian-backed propaganda forum, or a semi-accurate hype blog that only reports the facts when they align with its agenda. While companies like Google and Facebook have tried to come up with ways to flag shady content, a lot of stuff can still fall through the cracks, especially when we rely on human judgement. Using artificial intelligence and computer learning could be the key to helping us separate fact from fiction going forward.

To build his tool——Estela pulled data from two separate open-source projects that rate websites on a litany of different data points, from how right-or-left leaning the content is, to whether it espouses hateful points of view such as homophobia or sexism. is a project led by a research team at Merrimack College, and Media Bias Fact Check uses a detailed methodology to categorize each site. Both projects are also open source, allowing the public to play a role in the accuracy, kind of like how Wikipedia maintains a generally accurate record.