Abstract
In online communication it is difficult to know when something written is genuine or deceitful. There exist many reasons for someone to act less-than-truthful online (i.e., monetary gain, political gain) and detecting this behavior without any physical interaction is a difficult task. Additionally, deception occurs in several text-only domains and it is unclear if these various sources can be leveraged to improve detection. To address this, eight datasets were utilized from various domains to evaluate their effect on classifier performance when combined with transfer learning via intermediate layer concatenation of fine-tuned BERT models. We find improvements in accuracy over the baseline. Furthermore, we evaluate multiple distance measurements between datasets and find that Jensen-Shannon distance correlates moderately with transfer learning performance. Finally, the impact was evaluated of multiple methods, which produce additional information in a dataset’s text via named entities, on BERT performance and we find notable improvement in accuracy of up to 11.2%.
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Acknowledgement
Research partly supported by NSF grants 2210198 and 2244279, ARO grants W911NF-20-1-0254 and W911NF-23-1-0191, and a USDOT Cyber transportation center grant.
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Verma is the founder of Everest Cyber Security and Analytics, Inc.
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Triplett, S., Minami, S., Verma, R.M. (2025). Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection. In: Patil, V.T., Krishnan, R., Shyamasundar, R.K. (eds) Information Systems Security. ICISS 2024. Lecture Notes in Computer Science, vol 15416. Springer, Cham. https://doi.org/10.1007/978-3-031-80020-7_8
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