Summary
Detecting lies and trickery in stories or statements is very hard, especially without experts who know how deception works. The authors created ten patterns that model different kinds of deception, making it easier to spot when claims need closer checking. They built a web tool called DeceptionAnalyser that uses artificial intelligence to find important parts of a statement and then asks smart, critical questions based on those patterns to evaluate if something seems suspicious. Instead of saying outright if something is a lie, the tool highlights what needs more attention, helping people understand why a claim might be deceptive. They tested this method across many AI models and found it works well for clear cases and degrades gracefully when things are less obvious.
What this means in practice
- •For intelligence analysts: Use the tool to focus attention on possibly deceptive claims in intelligence reports with structured reasoning and AI support.
- •For fact-checking teams: Assist in flagging suspicious narratives for closer examination by highlighting argument patterns prone to deception.
Authors
Stefan Sarkadi, Xabier Garmendia, Jack Mumford, Trevor Bench-Capon
Abstract
Deception plays a central role in Intelligence operations, yet it remains difficult to analyse systematically without expert knowledge of reasoning patterns and cognitive manipulation. In computational argumentation, for instance, no scheme-level ground-truth corpora currently exist to support statistical validation. In this paper, we address this by introducing a set of ten argument schemes designed to model distinct forms of deception, each accompanied by structured premises and critical questions. In doing so, we introduce the first dedicated library of argumentation schemes specifically designed for deception analysis, providing a structured foundation for systematically modelling and analysing deception in narrative text. We then present \textit{DeceptionAnalyser}, a browser-based tool that implements these schemes through a two-stage methodology combining LLM-based premise extraction with critical-question-driven evaluation. Our aim is to provide a conceptual and methodological foundation for analysing deceptive reasoning in narrative text. This is precisely what we address in this paper by demonstrating how structured argumentation theory and AI-assisted analysis can support transparent, explainable assessments of potential deception. Because the schemes are designed to flag claims for scrutiny rather than to output a deception verdict, we do not benchmark classification accuracy; instead, we assess the \emph{reliability} of the methodology by measuring the consistency of the tool's premise and conclusion assessments across ten contemporary large language models and repeated runs. We find that scheme detection is highly stable for clear-cut deception and degrades gracefully, in interpretable ways, on more ambiguous intelligence-style narratives.