Machine Learning Series: Fighting Fake News

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Free Ventures is a nonprofit student-run accelerator at UC Berkeley for student-founded startups. Our internal team helps provide the brightest founders with equity-free funding, workspace, mentorship, academic units, and resources to scale.

Our monthly spotlight will feature innovative ways our companies are solving problems and insights into the Berkeley startup ecosystem.

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Read our full spotlight on a Batch X company here. Below are some highlights:

Machine Learning Series: Fighting Fake News

Socialist billionaire George Soros donating $50,000 to students in Berkeley to protest against Milo Yiannopoulos…was one of the many fake rumors spread at UC Berkeley in February of 2017. 

Students at Berkeley were shocked by the stories, and most assumed these fabricated statements were true. Two Berkeley undergrads — Ash Bhat and Rohan Phadte — felt especially perplexed by the spread of misinformation and decided to tackle the problem themselves.

They started RoBhat Labs in November of 2017 and joined Free Ventures’ Batch X in February of this year. They released their first product in late 2017 called which is a Twitter extension that detects bots spreading misinformation and propaganda. ContentBank is their second product that secures and actively takes down stolen and fake content across the internet.

In recent months, they partnered with the Democratic National Committee, have 50k+ active Twitter users, and had press coverage from MSNBC, Wired, CBS and many more. We sat down with them for an interview last week to learn more about their experience as student founders. 

Q: How have advances in machine learning helped you address Fake News?

Ash: “In terms of our core tech, we’re using ML for detecting high level features on images and content media such as videos. We very quickly compare between other versions of content to determine whether that content is misrepresented or doctored in some sort of way. An example was an NFL player dancing in the locker room. Someone photoshopped an NFL player with a burning flag, which soon became a viral photo, even though it was completely fake. With our tech you can use ML to easily detect it was manipulated and find out where it came from. 

Q: How does ML facilitate fake news detection?

Ash: “ML is really good at coming to these nuanced decisions. A good way of thinking about it is we’re trying to take the expertise it would require for someone to look through an account, understand the different characteristics that make an account a bot, and then distill that into a statistical model that anyone can download. All of a sudden, you have the power of an expert at the touch of a button.”

Q: Do you have any advice on how to establish business partnerships with larger organizations? 

Ash: “The two main principles we use are looking for pain points and aligning incentives. Fake news is a very clear pain point in the sense that people and company’s reputations are getting tarnished. To align incentives, we positioned ourselves by defining success as bringing democracy to a better place. Naturally making that tie in you almost make yourself metaphorical for a larger goal, which makes it easier for everyone around the table to come to an agreement.”

Be sure to read our full article to learn about their challenges as student founders, how they partnered with the DNC, and what resources they utilized on campus!