Merylin Monaro


Curriculum
Neuroscience, Technology, and Society, XXX series
Grant sponsor
UNIPD
Supervisor

Giuseppe Sartori
Co-supervisors

Mauro Conti, Luciano Gamberini
 
 

Dissertation abstract
Half the people in the Planet Earth are now on internet, surfing the web, keeping connection with the outside world, using online services and interacting in social networks. However, the spread of internet is going hand in hand with the growing malicious use of it. Creating fake social network profiles, wide spreading fake news, posting fake reviews, identity theft to perpetuate online financial frauds are only few examples. To face these problems, all the big internet companies, like Google and Facebook, are now taking the direction towards the online lie detection research. The present work is a contribution to online deception detection through the study of computer-user interaction. After a brief review of the current lie detection methods, focusing on their advantages and disadvantages for online application, a series of proof of concept experiments are reported. Experiments were conducted measuring indices deriving from three different tools of human-computer interaction: reaction times on keyboard, keystroke dynamics and mouse dynamics. Two strategies were used to increase liars’ cognitive load and facilitate the observation of distinctive features of deception: unexpected questions and complex questions. Experiments focused on the deception about identity, as it is a very hot issue and represents a current challenge for companies that provide online services. Participants were asked to respond lying or truth telling to questions that appeared on the computer screen, typing the response, clicking on it with the mouse or pressing one of two alternative keys on keyboard. Data collected from liars and truth-tellers’ responses were analyzed and used to train machine learning classification models. Classification accuracies in distinguishing liars from truth-tellers ranged from 80% to 95%, depending on the deceptive task. Results have proved that it is possible to spot liars analyzing their interaction with the computer during the act of lie. In particular, we demonstrated that keystroke dynamics is a very promising tool for covert lie detection and it is easily integrable with the online existing applications. Moreover, we confirmed that the cognitive complexity of the deceptive task increases the possibility to detect deception.