English

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

Computation and Language 2024-11-13 v5 Artificial Intelligence Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2311.10944,
  title  = {Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks},
  author = {Panfeng Li and Mohamed Abouelenien and Rada Mihalcea and Zhicheng Ding and Qikai Yang and Yiming Zhou},
  journal= {arXiv preprint arXiv:2311.10944},
  year   = {2024}
}

Comments

Accepted by 2024 5th International Conference on Information Science, Parallel and Distributed Systems

R2 v1 2026-06-28T13:24:51.515Z