Privacy-Aware Code-Mixed Cyberbullying Dataset for Session-Based Analysis

Cyberbullying behaviors manifest uniquely in different regions, shaped strongly by local slang, dialectal expressions, and cultural context. Code-mixed Chinese–English colloquial language (Cantonese) is commonly used in Hong Kong, Macau, and parts of southern China. Code-mixing is the use of multiple languages concurrently, and Cantonese text includes distinct phonetic, lexical, and syntactic features that are not exhibited in datasets developed for either Chinese or English applications. In this study, a privacy-aware code-mixed cyberbullying dataset (PCCD), containing 14,115 annotated tweets organized into 1668 sessions, was developed. Personally identifiable information and well-known identifiers, such as the names of famous celebrities, politicians, and organizations, were replaced with randomly generated dummy names. The anonymized data empirically demonstrated improved performance in terms of precision, recall, and F1 score, indicating a greater generalization ability when handling unseen participants. To the best of our knowledge, the PCCD is the first code-mixed Chinese–English dataset that includes abuser and victim identity annotation. Our dataset facilitates the development of robust cyberbullying detection tools that researchers and developers can use to accurately measure aggressiveness, attack frequency, and abuser–victim power imbalance in a dialogue session.