mlsanityData to 7 Oct 2026

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

arXiv 2311.10944 ↗First posted 18 Nov 2023cs.CL cs.AI cs.LGcited by 54

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Abstract (arXiv)

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.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Builds on 13 of its 65 references that are in the map. First posted on arXiv 18 Nov 2023.

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13 of 65 references.

References · 13

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  4. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  5. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 361
  6. 2022Can Language Understand Depth?cited by 66
  7. 2018Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagerycited by 42
  8. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  9. 2023Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidancecited by 24
  10. 2021DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusioncited by 17
  11. 2020Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videoscited by 7
  12. 2019Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approachcited by 4
1 more
  1. 2021Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularizationcited by 4

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Citing papers per year: 2024: 43; 2025: 11.

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