Detecting Deception in Political Debates Using Acoustic and Textual Features

Daniel Kopev, Ahmed Ali, Ivan Koychev, Preslav Nakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates. Thanks to the efforts of fact-checking organizations, it is possible to obtain annotations for statements in the context of a political discourse as true, half-True, or false. Starting with such data from the CLEF-2018 CheckThat! Lab, which was limited to text, we performed alignment to the corresponding videos, thus producing a multimodal dataset. We further developed a multimodal deep-learning architecture for the task of deception detection, which yielded sizable improvements over the state of the art for the CLEF-2018 Lab task 2. Our experiments show that the use of the acoustic signal consistently helped to improve the performance compared to using textual and metadata features only, based on several different evaluation measures. We release the new dataset to the research community, hoping to help advance the overall field of multimodal deception detection.

Original languageEnglish
Title of host publication2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages652-659
Number of pages8
ISBN (Electronic)9781728103068
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Singapore, Singapore
Duration: 15 Dec 201918 Dec 2019

Publication series

Name2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings

Conference

Conference2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019
CountrySingapore
CitySingapore
Period15/12/1918/12/19

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Keywords

  • computational paralinguistics
  • deception detection
  • disinformation
  • fact-checking
  • fake news
  • multi-modality
  • political debates

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Linguistics and Language
  • Communication

Cite this

Kopev, D., Ali, A., Koychev, I., & Nakov, P. (2019). Detecting Deception in Political Debates Using Acoustic and Textual Features. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings (pp. 652-659). [9003892] (2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASRU46091.2019.9003892