Why two Swiss-led data models predict a Trump win

BILOXI, MS – JANUARY 02: Republican presidential frontrunner Donald Trump speaks at the Mississippi Coast Coliseum on January 2, 2016 in Biloxi, Mississippi. Trump, who has strong support from Southern voters, spoke to thousands in the small Mississippi city on the Gulf of Mexico. Trump continues to split the GOP establishment with his populist and controversial views on immigration, muslims and some of his recent comments on women. (Photo by Spencer Platt/Getty Images)

Source: Swiss Info

Two teams of researchers in Switzerland say their data models based on search frequency and speech analysis forecast a win for Republican President Donald Trump in the US elections on Tuesday. Both teams’ models predicted Trump’s 2016 victory. What’s behind their approaches?

The teams’ outlooks for the 2020 presidential race stand at odds with widespread polling that shows the Democratic challenger, former Vice President Joe Biden, out in front generally by significant margins.

One team used internet and social media searches to gauge interest in the candidate. The other team tweaked an established forecasting system, which is based on economic data and incumbency, by also evaluating a candidate’s charisma.

“I did feel, seriously, like an idiot being about the sole person on the planet, perhaps one or two other research teams, that also said they think Trump would win [in 2016],” says John Antonakis, professor of organisational behavior at the University of Lausanne. Antonakis, together with Philippe Jacquart, professor at Emlyon business school in France, predicted the election based on the candidate’s charisma. “I really thought that we had gotten something fundamentally wrong.”

The problem with the polls

Both groups of researchers say polling, the traditional questioning of likely voters, faces several hurdles in gaining an accurate picture of the electorate. They cite several issues, including difficulty in contacting a representative sample of voters because of the move from land lines to mobile phones; the fact that responders may not answer truthfully; and the fact that people who are polled may not vote.

“These are serious statisticians,” Antonakis says of the pollsters, “but they have a very wicked problem to solve.”

Christoph Glauser, a mass media and political scientist, says he began to note the challenges in getting reliable poll data at least 15 years ago. He is the founder of the Institute for Applied Argumentation Research, IFAAR, a private research facility in Bern that creates computer-based systems for analysing digital media and online content.

Glauser, his research assistant Loris Schmid and Jacques Savoy, a professor of computer science at the University of Neuchâtel, led a team of scientists, economists, IT and data specialists and psychologists to develop a method of candidate evaluation based on internet searches and social media reach.

Unreliable polling “is why we started to develop API’s (application programming interfaces) for analysing what users do on the Internet in terms of searches,” he says. “These are small software packages that analyse, for example, what people really search for on Google, Twitter and Facebook.”

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