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Sometimes fake news is easy to spot because it’s so silly and unbelievable you know it’s made up. But sometimes it is not quite as easy to separate fact from fiction.
People are NOTORIOUSLY bad at spotting fake news articles when the line between fact and fiction gets a little blurrier. That’s why computer scientists are working on creating systems to detect those news stories that are made up to distract and confuse.
Some politicians use the term fake news as a way to describe a story that is considered damaging or negative to a particular position. More generally, fake news means news that is not supported by fact. Social media has brought the world together and makes it easy to look up just about any information you need online just as quickly as you can snap your fingers. However, it has also made it significantly easier for fake news to spread and reach people very quickly.
So how on earth are computer scientists going to combat the constant stream of fake news stories?
One of the aspects researchers are exploring is which factors are most accurate for indicating fake news. However, there’s not one set of factors or indicators that researchers agree upon.
Researchers agree that there are two major ways for people to spot fake news: consider what the author of the article is saying and how the author is saying it.
Computer scientists at Rensselaer Polytechnic Institute found that when compared with real news, fake news articles are generally shorter, more repetitive, and employ more adverbs and fewer quotes. Based on their findings, the researchers created a computer program to assess the truthfulness of articles: the number of nouns, number of quotes, redundancy, and word count. While their program was only found to be 71 percent accurate, it offers hope that computer scientists may be able to help us filter out some of the fake news that is constantly coming our way.