Linguistic Deception Detection – Models, Domains, Behaviors, Stylistic Patterns to Large Language Models (LLMs)

Deception in language—ranging from fake news and spam to phishing and rumor—has long been a tool for manipulation, exploiting linguistic ambiguity and psychological triggers to mislead readers. Deception spanned varied domains, yet shared common traits, which enabled the development of domain-independent detection methods that transferred knowledge across tasks using feature augmentation and multi-task learning. Psychological modeling further revealed how deception often plays on urgency, fear, and enticement. However, with the advent of Large Language Models (LLMs), the landscape of deception has shifted dramatically. These models can generate fluent, context-aware, and human-like text that often evades even SoTA detectors, blurring once-reliable cues of manipulation. Beyond mundane misuse to aid in fake reviews or partisan journalism, LLMs exhibit a more profound ability: generating scientifically coherent, logically sound ideas that closely resemble human reasoning. While this raises serious concerns around idea attribution and originality within the broader deception landscape, it also opens an opportunity to understand the underlying thought patterns of LLMs—moving beyond shallow stylistic rephrasings to deeper cognitive structures. This dissertation unifies classical deception paradigms with emerging LLM-centric challenges, offering a comprehensive framework to detect and reason about deception in its many evolving forms.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2025

Author dc:creator

• Shahriar, Sadat 1993-

Advisor dc:contributor.advisor

• Mukherjee, Arjun

Committee members dc:contributor.committeemember

• Eick, Christoph F
• Gnawali, Omprakash
• Prasad, Saraubh

• Computer science

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/19579

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/19579