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Domain-independent deception: a new taxonomy and linguistic analysis
Rakesh M Verma1, Nachum Dershowitz2, Victor Zeng3,4
1Department of Computer Science, University of Houston, Houston, TX, United States.
Frontiers in Big Data
|October 17, 2025
Summary
Researchers identified common linguistic cues for deception across various online attacks. This finding demonstrates significant knowledge transfer, paving the way for more effective, domain-independent deception detection systems.
Area of Science:
- Computer Science
- Artificial Intelligence
- Natural Language Processing
Background:
- Deceptive online attacks like fake news and phishing are prevalent.
- Existing detection methods are often domain-specific.
- Research into domain-independent deception detection is emerging.
Purpose of the Study:
- To investigate domain-independent deception.
- To develop a new computational definition and taxonomy of deception.
- To build a comprehensive dataset for deception research.
Main Methods:
- Defined deception computationally and created a new taxonomy.
- Compiled a comprehensive real-world dataset.
- Employed classical and deep learning models to analyze linguistic features across domains.
Main Results:
- Identified common linguistic cues indicative of deception.
- Provided evidence for knowledge transfer across different deception domains.
- Demonstrated the effectiveness of domain-independent models.
Conclusions:
- Common linguistic patterns exist across various deceptive online content.
- Knowledge transfer is feasible, enabling more generalized deception detection.
- Future work should explore advanced models for enhanced detection capabilities.
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