AI News, Can Data Science Deliver a Fake News Detector?

Can Data Science Deliver a Fake News Detector?

Let’s assume purveyors of fake news stories are very much aware they are not engaging in ethical journalism (to put it mildly).

So, couldn’t data scientists and engineers come up with a “Fake News Detector” based on nothing but the web data trail news articles generate?

We’re hoping that is about to change, thanks to several teams working with data pulled from the webhose.io web data feed API.

Any data scientist will tell you that creating a machine model for fake news requires at least two distinct datasets;

You’d need to feed thousands of fake news items into a machine to create a data-driven model what constitutes a fake news story.

For their graduation project initially designed as a fake news detector, Nova Fallen, Sebi Lozano, Scott Freeman, and Sacha Best changed their project goal from fake news detection to indication of political bias.

Approach 2: Identify Known Sources of Fake News Several teams, including the crowdsourced Kaggle community, are working on fake news detection based on known flagged sources of unverified news content.

CrossCheck CFO Jay Khurana explained the team’s approach: “We know that websites devoted to satire, spoof, and hoax content are publishers of known false content, it’s worth adding them to a source list, especially for algorithm training purposes.” Assuming they’re maintained, these sources could contribute large datasets you can use to develop sophisticated machine learning models.

It gets even trickier when you consider the modern news game rewards sensational headlines and stories that are at the edge of conventional wisdom.

You have to read the entire article and watch the video to discover that it is indeed an amazing story 41 years in the making.

As web data-driven news publisher  Vocative reported last week, several Scandinavian outlets announced they would not participate in the long time tradition of April Fool’s items.

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