Cyberbullying, as a global psychological health threat, faces challenges in accurate
identification due to the biases of traditional self-report methods and obstacles to help-seeking.
This study constructs a Chinese cyberbullying victim dictionary based on social media big data,
psychological feature dictionaries, and cyberbullying questionnaires to enhance the accuracy of
victim identification, providing the possibility for timely intervention. The dictionary's validity
was confirmed through a correlation analysis between word frequency statistics of 500 Weibo
posts and expert ratings (n=3). To assess whether DeepSeek-R1 and GPT-4o could aid in the
dictionary compilation process, the study replicated the manual creation process and compared
their performance to expert evaluations using Kappa consistency coefficients, intraclass
correlation coefficients (ICC), recall rates, and precision rates under both simple and complex
instructions.Results showed that: (1) The dictionary, consisting of 442 words across three
dimensions—cyberbullying methods, harm perception, and coping strategies—demonstrated high
validity in identifying cyberbullying victimization expressions (r = 0.870, p < 0.001) and
assessing victimization severity (r = 0.533, p < 0.001); (2) In text classification, DeepSeek-R1
performed well on small samples (Kappa = 0.775-0.781), but its performance dropped with a
large dataset of 12,600 posts. Both models showed discrepancies in vocabulary selection and
weight assignment (lowest Kappa = -1); however, DeepSeek-R1 showed moderate consistency in
dimensional categorization (Kappa = 0.659-0.660) and outperformed GPT-4o. This study is the
first to develop a Chinese cyberbullying victim dictionary, highlighting AI's potential in
dictionary construction and suggesting human-AI collaboration for large-scale data tasks.