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Dynamic Changes in Cognitive Processes Among Online Fraud Victims in China: Evidence from Text Analysis

Yiwen Wu ; Jie Wang ; Liang Xu (2026) — Deviant Behavior

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DOI: 10.1080/01639625.2026.2705349 (opens in a new tab)

Country:   China
Tags:   China, Cognitive Processes, Liwc, Online Fraud, Pig Butchering Fraud, Romance Scams, Text Analysis, Victim Narratives
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Synopsis

            
This publication reports on a study of how victims’ own narratives about online fraud in China reveal evolving cognitive processes as fraud unfolds. The authors analyzed 1,217 self-authored posts from Chinese social platforms, using LIWC-based measures to track seven cognitive-semantic categories (including Insight, Causation, Differentiation, Discrepancy, Tentative, Certainty, and first-person pronouns) across three stages: early perceived plausibility, middle engagement, and final fraud realization. The aim was to map a cognitive-semantic trajectory that mirrors mental processing and to see whether patterns differ by fraud type. The results indicate clear stage-based linguistic dynamics. In the early stage, victims employ more tentative and exploratory language, signaling uncertainty and cognitive vigilance. In the middle stage, language reflects deeper cognitive involvement, with greater use of logical reasoning and emotionally engaged expressions. In the final stage, increased certainty and emotional resolution mark the recognition of deception and cognitive-emotional repair. Across fraud types, economically motivated schemes tend to elicit more rationalizing and self-justifying language, while trust-dependent schemes show more socially and emotionally oriented expressions. The authors interpret these patterns as evidence of distinct cognitive trajectories shaped by the fraud context, with language functioning as a tool for meaning-making and self-repair after deception. The study also discusses implications for fraud detection and risk assessment through linguistic monitoring, and notes limitations such as retrospective narratives, potential recall biases, and data drawn from a single secondary dataset. It suggests future work could broaden data sources and incorporate longitudinal or real-time data to better capture in-situ cognitive dynamics.

          

        
                  

            
Identified Gaps

            
Prior work has paid limited attention to victims’ evolving cognitive-semantic trajectories during fraud and distinctions across fraud types. The study also identifies gaps in real-time evidence, deep semantic analysis, localized tools for indirect Chinese expression, demographic and gender data, links to reporting and legal outcomes, and fine-grained classification of hybrid and AI-enabled fraud.

          

        
                  

            
Methods

            
Secondary analysis of 1,217 publicly available first-person online-fraud narratives from Zhihu and Douban in China (1,606,683 words). Posts were coded by fraud type and segmented into early plausibility, middle engagement, and final realization stages. Chinese LIWC2015 categories were extracted using Jieba tokenization, normalized for text length, and analyzed with one-way ANOVAs and Games-Howell post hoc comparisons across stages and fraud types.

          

        
                  

            
Limitations

            
Narratives were retrospective and may contain memory bias. The study used a single secondary dataset from two Chinese social-media platforms, creating platform-specific self-selection and limited representativeness. Anonymous narratives lacked reliable gender data and could not be linked to reporting, investigation, prosecution, or court outcomes. The economic-versus-trust distinction and an extensive “Other fraud” category may obscure hybrid scams. LIWC word-level analysis cannot capture deep semantics, while indirect and metaphorical Chinese expression may be insufficiently represented by the dictionary.

          

        
                  

            
Future Work

            
Use longitudinal or real-time data; apply semantic clustering or topic modeling for fraud classification; integrate semantic networks, contextual embeddings, and localized Chinese dictionaries; triangulate social-media narratives with police, hotline, counseling, interview, or survey data; collect demographic data to assess gender; connect narratives to reporting and justice outcomes; and use larger multi-source data to identify emerging hybrid and AI-enabled fraud subtypes.

          

              

    
    

        
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