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

B. M. DePaulo, J. J. Lindsay, B. E. Malone, L. Muhlenbruck, K. Charlton, and H. Cooper, “Cues to deception,”

2003

Earlier work this paper cites.

N. R. Council,

2003

Earlier work this paper cites.

L. Zhou, J. K. Burgoon, D. P. Twitchell, T. Qin, and J. F. Nunamaker, Jr., “A comparison of classification methods for predicting deception in computer-mediated communication,”

2004

Earlier work this paper cites.

J. Hirschberg, S. Benus, J. M. Brenier, F. Enos, S. Friedman, S. Gilman, C. Girand, M. Graciarena, A. Kathol, L. Michaelis

2005

Earlier work this paper cites.

C. F. Bond and B. M. DePaulo, “Accuracy of deception judgments,”

2006

Earlier work this paper cites.

M. Ott, Y. Choi, C. Cardie, and J. T. Hancock, “Finding deceptive opinion spam by any stretch of the imagination,” in

2011

Earlier work this paper cites.

G. Ganis, J. P. Rosenfeld, J. Meixner, R. A. Kievit, and H. E. Schendan, “Lying in the scanner: Covert countermeasures disrupt deception detection by functional magnetic resonance imaging,”

2011

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in

2012

Earlier work this paper cites.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,”

Original

2013

Earlier work this paper cites.

M. Abouelenien, V. Pérez-Rosas, R. Mihalcea, and M. Burzo, “Deception detection using a multimodal approach,” in

2014

Cited alongside, same era.

Y. Kim, “Convolutional neural networks for sentence classification,” in

2014

Cited alongside, same era.

B. A. Rajoub and R. Zwiggelaar, “Thermal facial analysis for deception detection,”

2014

Cited alongside, same era.

C. Sun, Q. Du, and G. Tian, “Exploiting product related review features for fake review detection,”

2016

Cited alongside, same era.

——, “Detecting deceptive behavior via integration of discriminative features from multiple modalities,”

2017

Cited alongside, same era.

T. Baltrusaitis, C. Ahuja, and L. Morency, “Multimodal machine learning: A survey and taxonomy,”

X. Chen, A. T. Z. Kasgari, and W. Saad, “Deep learning for content-based personalized viewport prediction of 360-degree vr videos,”

2020

Later among the works it cites.

R. Zhang, Z. Zeng, Z. Guo, and Y. Li, “Can language understand depth?” in

2022

Later among the works it cites.

J. Yao, T. Wu, and X. Zhang, “Improving depth gradientcontinuity in transformers: A comparative study on monocular depth estimation with cnn,”

Original

2023

Closest in time.

T. Deng, H. Xie, J. Wang, and W. Chen, “Long-term visual simultaneous localization and mapping: Using a bayesian persistence filter-based global map prediction,”

2023

Closest in time.

D. Li, Z. Tan, T. Chen, and H. Liu, “Noise-robust fine-tuning of pretrained language models via external guidance,”

Original

2023