Fake News: Machine Learning not the answer in combating the spread of mis-information

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It had been hoped that Machine Learning would be the answer to identifying fake news stories, enabling them to be stopped before they spread misinformation.

While it’s relatively easy for computers to create fake news without much input from humans, current machine learning models aren't yet up to the task of distinguishing false news reports according to two new papers by MIT researchers.

After different researchers showed that computers can convincingly generate made-up news stories without much human oversight, some experts hoped that the same machine-learning-based systems could be trained to detect such stories. But MIT doctoral student Tal Schuster's studies show that, while machines are great at detecting machine-generated text, they can't identify whether stories are true or false.

Schuster says the problem lies with the database used to train computers to spot fake news. That database is called Fact Extraction and Verification (FEVER). Schuster found that ML-taught computers struggled to interpret negative statements about a subject even when the computers could easily interpret positive statements. As Axios reports:

So what is the solution to the rising problem that is fake news? Well, it might still be machine learning... Just not anytime soon.

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