Machine Learning, Graphs and the Fake News Epidemic (Part 1)

The Pew Research Center found that two-thirds of US adults believe fake news has left them confused about basic facts, with a quarter admitting to sharing fake news themselves. Social media is often blamed for the spread of fake news, and an analysis by BuzzFeed News showed that top-performing fake news articles on Facebook resulted in more shares and engagement than major news sources like the New York Times. Experts argue that the problem requires human judgment and cannot be solved solely through technology. Instead, machine learning algorithms can be used to flag potentially misleading news, which is then reviewed by humans. Graph database technology has been shown to be effective in recognizing and leveraging connections in large amounts of data, and could be used to build a "news graph" that provides additional insight into the bias and credibility of individual articles and their sources.

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