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电信网络诈骗被害程度的影响因素——基于个体特征和社会环境的双层嵌套分析 / The factors influencing the severity of telecom network fraud victimization: A dual-level nested analysis based on individual characteristics and social environment

吴鹤群 / WU Hequn ; 王强 / WANG Qiang ; 郑滋椀 / ZHENG Ziwan ; 李国军 / LI Guojun (2026) — 地理研究 / Geographical Research 45(4), 1127–1142

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DOI: 10.11821/dlyj020240772 (opens in a new tab)

Country:   China
Tags:   Community Environment, Large Loss Victimization, Population Mobility, Prevention, Romance Related Scams, Telecom Network Fraud
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Synopsis

            
This study examines what factors shape the severity of telecom network fraud victimization, focusing on how individual characteristics and the social environment interact to influence whether a case results in a large loss. Using 2021 data from Xiaoshan District, Hangzhou, the authors build a two-level framework that links individual-level factors (victim sex, age, household registration, case type, and case duration) with community-level variables (the share of migrants, the share of leased housing, and the share of residents aged 19–45). The research treats large-loss outcomes as a binary variable, with a threshold set at 6,000 yuan to capture cases meeting the “large” qualification in local law. A generalized multi-level model (HGLM) is employed to account for nesting of cases within communities and to test cross-level interactions. Key findings indicate that large-loss fraud cases cluster in similar geographic areas as overall fraud, but the concentration is higher for large-loss incidents. There are significant between-community differences in loss severity, and several individual factors are associated with higher odds of large losses: being female, and being part of certain resident groups, with migrants also showing elevated risk in some specifications. Case duration and the type of fraud are related to large-loss outcomes as well. Beyond these main effects, community context moderates risk: higher shares of floating population amplify the likelihood of large losses for women and younger adults and increase the probability of large losses in recruitment- or dating-related schemes. A higher share of leased housing strengthens the link between case duration and large losses, while communities with more 19–45-year-olds raise male risk and heighten duration effects, yet appear to reduce large-loss outcomes in shopping and dating fraud categories. The authors conclude that the results can inform targeted prevention strategies by highlighting how both individual characteristics and neighborhood environments shape the risk of substantial fraud losses.

          

        
                  

            
Identified Gaps

            
Prior research insufficiently considered case characteristics, especially whether fraud type and duration affect large-loss victimization. It also lacked analysis of cross-level interactions between victim characteristics and community conditions and paid limited attention to loss value rather than victimization occurrence. Remaining gaps include whether the built environment affects large-loss victimization and how victims’ online behavior, information pathways, and daily activity patterns shape fraud risk and severity.

          

        
                  

            
Methods

            
The study analyzed all reported telecom network fraud cases in Xiaoshan District, Hangzhou, China, in 2021. It retained 5,094 cases in 303 communities with at least five cases each. Large-loss victimization was defined as a loss of at least 6,000 yuan. Kernel density estimation mapped spatial clustering, and generalized hierarchical linear modeling estimated individual- and community-level effects and cross-level interactions.

          

        
                  

            
Limitations

            
Data availability and concerns about overfitting, multicollinearity, model generalizability, interpretability, and stability led the authors to limit the number of independent variables, leaving relatively few individual-level measures. The study also did not test built-environment effects on large-loss victimization or directly measure victims’ online behavior, information access, daily activities, or spatiotemporal behavior.

          

        
                  

            
Future Work

            
Future research should test whether built-environment conditions affect the probability of large-loss victimization. It should also apply routine activity theory and crime pattern theory to examine victims’ internet use, information-acquisition routes, daily activity patterns, and spatiotemporal behavior in relation to fraud probability and loss severity.

          

              

    
    

        
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