Abstract
Deception detection -identifying when someone is trying to cause someone else to believe something that is not true- is a hard task for humans. The task is even harder for automatic approaches, that must deal with additional problems like the lack of enough labeled data. In this context, transfer learning in the form of cross-domain classification is a task that aims to leverage labeled data from certain domains for which labeled data is available to others for which data is scarce. This paper presents a study on the suitability of linguistic features for cross-domain deception detection on multimodal data. Specifically, we aim to learn models for deception detection across different domains of written texts (one modality) and apply the new knowledge to unrelated topics transcribed from spoken statements (another modality). Experimental results reveal that by using LIWC and POS n-grams we reach a in-modality accuracy of 69.42%, as well as an AUC ROC of 0.7153. When doing transfer learning, we achieve an accuracy of 63.64% and get an AUC ROC of 0.6351.
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Notes
- 1.
They were all implemented on Weka using the default values.
- 2.
Tables containing all the results gotten from each classifier can be found in Appendix A, but DT are not included since they were constantly outperformed by the other classifiers.
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Appendix A Results achieved by each classifier for all the proposed representations in the 15 study cases
Appendix A Results achieved by each classifier for all the proposed representations in the 15 study cases
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Rill-García, R., Villaseñor-Pineda, L., Reyes-Meza, V., Escalante, H.J. (2019). From Text to Speech: A Multimodal Cross-Domain Approach for Deception Detection. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds) Pattern Recognition and Information Forensics. ICPR 2018. Lecture Notes in Computer Science(), vol 11188. Springer, Cham. https://doi.org/10.1007/978-3-030-05792-3_16
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