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Trust-Aware Detection of Malicious Users in Dating Social Networks

Xingfa Shen ; Wentao Lv ; Jianhui Qiu ; Achhardeep Kaur ; Fengjun Xiao ; Feng Xia (2022) — IEEE Transactions on Computational Social Systems

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DOI: 10.1109/tcss.2022.3174011 (opens in a new tab)

Country:   China
Tags:   Chat Analysis, Class Imbalance, Fake Profiles, Malicious User Detection, Online Dating, Romance Scams, Trust Model, Xgboost
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Identified Gaps

            
Research on malicious-user detection in dating social networks is limited, partly because research data are scarce and restricted by privacy and platform access. The paper also identifies insufficient guidance on which user information should be used or avoided in detection models.

          

        
                  

            
Methods

            
The study develops a trust-aware malicious-user detection framework using data from a Chinese dating site. It extracts features covering profile completeness, behavioral anomalies, chat content, photo authenticity, interaction evaluations, and matrix-factorized latent features. It balances highly imbalanced labels with DKB_SMOTE and compares six classifiers. A dataset of 6,230 users, including 78 labeled malicious users, was repeatedly evaluated using precision, recall, and F-score.

          

        
                  

            
Limitations

            
The authors state that model accuracy is low. They attribute this to a relatively small dataset and sample, and to adverse effects of class imbalance that are difficult to eliminate. They also state that the model lacks strong theoretical support for deciding which available information should be included or excluded.

          

        
                  

            
Future Work

            
Test the model on a widely recognized fraud dataset; explore graph neural network methods to address dataset imbalance; and develop stronger theoretical guidance, potentially drawing on psychology and sociology, for selecting appropriate user information features.

          

              

    
    

        
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