Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks

Deception detection is gaining increasing interest due to ethical and security concerns. This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and...

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Bibliographic Details
Published in:	arXiv.org
Main Authors:	Li, Panfeng, Abouelenien, Mohamed, Mihalcea, Rada, Ding, Zhicheng, Yang, Qikai, Zhou, Yiming
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 12.11.2024
Subjects:	Artificial neural networks Data transmission Linguistics
ISSN:	2331-8422
Online Access:	Get full text
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Summary:	Deception detection is gaining increasing interest due to ethical and security concerns. This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and one falsified response from each subject about each topic. In particular, we make three main contributions. First, we extract linguistic and physiological features from this data to train and construct the neural network models. Second, we propose a fused convolutional neural network model using both modalities in order to achieve an improved overall performance. Third, we compare our new approach with earlier methods designed for multimodal deception detection. We find that our system outperforms regular classification methods; our results indicate the feasibility of using neural networks for deception detection even in the presence of limited amounts of data.
Bibliography:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.2311.10944