Posted by : Anonymous Thursday, September 21, 2017


            Every year, millions of people have their identities stolen. There’s no foolproof way to pinpoint fakers, but thanks to Italian researchers, investigators may soon have another tool at their disposal—a way to suss out frauds and other liars online with just a few clicks of a mouse.

            Traditional methods of lie detection include face-to-face interviews and polygraphs that measure heart rate and skin conductance. But they can’t be done remotely, or with large numbers of people. Researchers have come up with effective computer-based tests that measure reaction time in response to true and false personal information. For the tests to work, though, experimenters have to know the truth in advance.

            They asked 20 volunteers to memorize the details of a fake identity and assume it as their own. The subjects then answered a set of yes-or-no questions using a computer, as did 20 truth-telling volunteers. Questions included things like: “Is Giulia your name?” and “Were you born in 1995?” Researchers recorded each answer and measured how the subjects’ mouse cursors moved, from the bottom middle of the screen to “yes” and “no” buttons in the top two corners.

           Because liars can get to be as good as the rest of us at telling the truth, the researchers threw a wrench into their experiment. In addition to the 12 expected questions, they asked 12 unexpected questions based on the volunteers’ new identities. For example, they asked about a person’s zodiac sign, based on their birth date. And they asked about the capital city of the subject’s presumed region. A fraud might have memorized a fake birthday, but not known the corresponding zodiac sign, or been able to calculate it quickly enough. “We’ve found that if people rehearse lies, lying can be as easy as telling the truth,” says Bruno Verschuere, a forensic psychologist at the University of Amsterdam who was not involved in the research, “except when you ask unexpected questions.”

          The experimenters trained a computer to sort liars from truth tellers using the number of incorrect answers they gave. The team’s four machine-learning algorithms ranged in accuracy from 77.5% to 85%. But when the researchers included features of the mouse paths—such as deviation from a straight line—in their training materials, computers were able to successfully pick out the liars 90% to 95% of the time, the researchers reported.

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