Dartmouth engineering researchers have developed a new approach for detecting a speaker’s intent to mislead.
The approach’s framework, which could be developed to extract opinion from “fake news,” among other uses, was recently published as part of a paper in Journal of Experimental & Theoretical Artificial Intelligence.
Although previous studies have examined deception, this is possibly the first study to look at a speaker’s intent.
The researchers posit that while a true story can be manipulated into various deceiving forms, the intent, rather than the content of the communication, determines whether the communication is deceptive or not.
For example, the speaker could be misinformed or make a wrong assumption, meaning the speaker made an unintentional error but did not attempt to deceive.
“Deceptive intent to mislead listeners on purpose poses a much larger threat than unintentional mistakes,” said Eugene Santos Jr., co-author and professor of engineering at Thayer School of Engineering at Dartmouth. “To the best of our knowledge, our algorithm is the only method that detects deception and at the same time discriminates malicious acts from benign acts.”
The researchers developed a unique approach and resulting algorithm that can tell deception apart from all benign communications by retrieving the universal features of deceptive reasoning.
However, the framework is currently limited by the amount of data needed to measure a speaker’s deviation from their past arguments; the study used data from a 2009 survey of 100 participants on their opinions on controversial topics, as well as a 2011 dataset of 800 real and 400 fictitious reviews of the same 20 hotels.
Santos believes the framework could be further developed to help readers distinguish and closely examine the intent of “fake news,” allowing the reader to determine if a reasonable, logical argument is used or if opinion plays a strong role.
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