Domain Independent Deception Detection: Feature Sets, LIWC Efficacy, and Synthetic Data Challenges-Reference-Cited by-同舟云学术

Domain Independent Deception Detection: Feature Sets, LIWC Efficacy, and Synthetic Data Challenges

Published:2024-06-19 Issue: Volume:77 Page:59-68
ISSN:
Container-title:Proceedings of the 10th ACM International Workshop on Security and Privacy Analytics
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Author:

Hanks Casey¹^ORCID,Ko Shanina¹^ORCID,Nguyen Emily²^ORCID,Verma Rakesh M.³^ORCID

Affiliation:

1. University of Maryland, Baltimore County, Baltimore, Maryland, USA

2. University of Maryland Baltimore County, Baltimore, Maryland, USA

3. University of Houston, Houston, Texas, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3643651.3659895

Reference21 articles.

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2. Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. FLAIR: An easy-to-use framework for state-of-the-art NLP. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics (demonstrations). 54--59.

3. Dainis Boumber, Fatima Zahra Qachfar, and Rakesh M. Verma. 2024 a. Domain-Agnostic Adapter Architecture for Deception Detection: Extensive Evaluations with the DIFrauD Benchmark. In Proceedings of The 2024 Joint International Conference On Computational Linguistics, Language Resources And Evaluation (LREC-COLING 2024).

4. Blue Sky: Multilingual, Multimodal Domain Independent Deception Detection

5. Ryan L Boyd, Ashwini Ashokkumar, Sarah Seraj, and James W Pennebaker. 2022. The development and psychometric properties of LIWC-22. Austin, TX: University of Texas at Austin (2022), 1--47.