@inproceedings{7310aa27baed467db179015edb28befb,
title = "Deceit Detection: Identification of Presenter's Subjective Doubt Using Affective Observation Neural Network Analysis",
abstract = "We live in a world surrounded with 'fake news' and manipulated information, so a system assisting people with knowing what information to trust would be beneficial. Our research investigates situations where the presenters themselves have doubts about the information they are delivering, and we detect this via advanced affective computing techniques. To this end we examine the physiological foundations for observer recognition of the doubt effect: the subjective belief or disbelief of a presenter in some information he or she is presenting. Firstly, we construct stimulus videos that display presenters delivering information about which we manipulate their degree of doubt. We then show these stimuli to observers, and record four of their physiological signals. We find that a generalised neural network trained with physiological features is more accurate in differentiating the presenters' doubt/manipulated belief when compared with the same observers' own conscious judgments. The affective recognition performance improves when we analyse the physiological signals using multi-task learning techniques to train personalised and group personalised neural networks. The ability to recognise this doubt effect derives from observers' fundamental emotional reactions to the viewed stimuli, reflected in their physiological responses, and learnt by our neural networks. We believe this system using observer physiological signals collected in real life could reveal accurate and hidden audience distrust, which could in turn lead to enhanced truthfulness in future public- presented statements.",
keywords = "Audiences, Blood volume pulse, Doubt, Galvanic skin response, Information veracity, Multitask learning (MTL), Neural networks (NN), Presenters, Pupillary dilation, Skin temperature, Subjective belief, Trust",
author = "Xuanying Zhu and Tom Gedeon and Sabrina Caldwell and Richard Jones and Xiaohan Gu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 ; Conference date: 11-10-2020 Through 14-10-2020",
year = "2020",
month = oct,
day = "11",
doi = "10.1109/SMC42975.2020.9283210",
language = "English",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3174--3181",
booktitle = "2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020",
address = "United States",
}