No Surprises Here: Algorithm Proves to Be Better Than Humans in Detecting Fake News

Outer Places 3/18/19

One of sharpest truths you learn as you realize that arguing with people – either on the Internet or elsewhere – is a waste of time and energy is that for the most part, people will not be swayed by facts, no matter how thoroughly demonstrated, rooted in science, or proven. Similarly, people will believe all kinds of things that aren’t true, despite all evidence to the contrary. For some, it seems as if the more outlandish the claim, the more dedicated they are to not only believing in it, but in spreading it. Nowhere is this gullibility better evidenced than in the online proliferation of dezinformatsiya (disinformation) – also known as “Fake News” – by Russian provocateurs seeking to deepen social divisions in the United States preceding, during, and following the 2016 American presidential election. Fortunately for our republic, this type of enemy propaganda can be detected by special types of artificial intelligence (AI) that can analyze text, vet sources, and otherwise probe articles, memes, claims, and other sources of boldfaced BS that people consume over the course of their participation in the 24/7 news cycle.

Now, a group of researchers at the University of Michigan have designed an algorithm that not only detects fake news, it does it better than humans do. The researchers, led by U-Michigan engineering professor Rada Mihalcea, utilized a linguistic analysis approach to train their AI in detecting disinformation. Samples were created by writers who took real news stories and created fake stories out of them, leaving certain text intact, while inventing facts and attempting to mimic the voice of the original article and fed to the AI along with real, unchanged news stories. Mihalcea’s team found that their algorithm accurately detected fake news stories 76% of the time, as compared to a human rate of 70%. Their results were described in a paper entitled “Automatic Detection of Fake News,” which will be presented at the 27th International Conference on Computational Linguistics, to be held in Santa Fe, New Mexico in August of this year.

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