Deception Detection in Digital Text via Psycho-linguistic Analysis
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Solution Overview
Problem
Current systems are inadequate in detecting deception in digital human communications, particularly on the Internet, as they often rely on network properties and have limited effectiveness in identifying deceptive intentions and hidden identities, leading to significant economic and psychological impacts.
Innovation Solution
A system that uses a computer programmed with software to analyze text messages for psycho-linguistic cues, IP geo-location, gender analysis, authorship similarity, and detection of coded/camouflaged messages, providing a graphical user interface for users to assess the deceptiveness of text messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network property-based detection methods are used, then the system is simple to implement, but the detection precision and effectiveness against deceptive intentions are limited
Solution Approach 1:
The system segments deception detection into multiple independent analysis modules: psycho-linguistic analysis of text content, IP geo-location analysis, gender analysis, authorship similarity analysis, and coded message detection. Each module processes specific features independently and their results are integrated to form a comprehensive deception assessment, thereby improving detection precision through multi-dimensional analysis.
Solution Approach 2:
The system merges multiple analysis techniques (psycho-linguistic analysis, statistical analysis, IP geo-location, gender identification, authorship verification) into a unified deception detection framework. This combination allows the system to cross-validate findings across different analysis dimensions, significantly enhancing detection precision beyond what any single method could achieve alone.
2Reliability
If multiple analysis techniques are combined to improve detection accuracy, then the detection rate increases, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by first evaluating psycho-linguistic cues and basic text features, which can quickly identify obvious deceptive patterns. Based on these initial results, the system selectively activates more computationally intensive analyses (such as authorship similarity or geo-location verification) only when needed, thereby maintaining high detection reliability while reducing average processing time for clearly identifiable cases.
Solution Approach 2:
The system implements a tiered analysis approach where not all analysis techniques are applied to every message. Instead, it applies a core set of analyses to all messages and reserves more resource-intensive techniques for cases showing suspicious patterns or high-stakes communications, achieving reliable detection with optimized resource utilization.
Data Source
AI summary
An apparatus and method for determining whether text is deceptive has a computer programmed with software that automatically analyzes text in digital form by at least one of statistical analysis of psycho-linguistic cues, IP geo-location, gender analysis, authorship analysis, and analysis to detect coded/camouflaged messages. The computer has truth data against which the text message can be compared and a graphical user interface. The computer may be connectable to the Internet and may obtain the text automatically. Speech-to-text software may be used to convert verbal messages to text for analysis. The system may be made available on a webpage, web service, on a computer or by a wireless device. The text may be emails, website content, tweets. In one embodiment, the system detects coded messages.