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The term has been invoked by everyone from the president to Pope Francis. You鈥檝e seen examples of it on social media in the form of memes promoting conspiracy theories or stories shared from websites of dubious origins. We鈥檙e talking, of course, about 鈥渇ake news.鈥�

Once the provenance of Weekly World News and other supermarket tabloids (we miss Bat Boy), pseudo news now resembles a pandemic, spread by bots, propagandists, and the gullible via Twitter, Facebook, and other social media platforms. Fake news may or may not sway elections, depending upon which reports you believe, but there鈥檚 no denying its ability to move markets and threaten brands large and small.

Last year, cryptocurrency platform Ethereum lost $4 billion鈥�20 percent of its market value鈥攁fter social media users fanned a false rumor that one of its co-creators had died in a car crash. Similarly, Starbucks took quick action to shut down a false promotional campaign that emerged from 4chan鈥檚 online mischief-makers before it could cause any financial damage. These days, the potential exists for artificial intelligence to be fooled by artificial news as well. When a false story suggesting Google planned to buy Apple for $9 billion circulated on the Dow Jones Newswire, Apple鈥檚 stock price experienced a momentary surge thanks to trading controlled by computer algorithms that scan Twitter and news headlines for insights.

鈥淰igilance and speed of response are essential in fighting fake news,鈥� Signal Media founder David Benigson says. But what if companies and news consumers didn鈥檛 have to be reactive combatants?

That鈥檚 where 91看片 College business analytics associate professor Ashish Gupta enters the fray. Gupta and 91看片 graduate students have been building a large repository of Twitter data鈥攏early 4 terabytes thus far鈥攁nd taking that 鈥渢reasure trove of data鈥� to develop algorithms that flag falsities.

鈥淔ake news is a controversial topic,鈥� Gupta says, 鈥渂ut it鈥檚 worthy of research and consideration.鈥�

Gupta is interested in patterns rather than politics, and using 鈥渄eep learning鈥� to unmask untruths. Gupta says API鈥攁pplication, programming, and interface鈥攖ools help eliminate 鈥渘oise鈥� (advertisements and comments) from social media postings as a means of determining veracity. The analysis of 鈥渘ative language鈥� identifies tell-tale rhetorical signs鈥攃onstruction of the message, certain phrases鈥攖hat can flag a tweet as a purveyor of fake news.

According to the Pew Research Center, 64 percent of US adults say fabricated news stories cause confusion about the basic facts of current events and issues. While 39 percent feel confident they can recognize fake news, 23 percent admit to having shared a false story.

Gupta鈥檚 work may provide two outcomes鈥攑roviding companies with a new way to protect their reputations and bottom lines, and producing better-informed social media users.