TemSoGraph: Learning temporal social graphs for cyberbullying prediction

Cyberbullying is a pervasive issue on online platforms, yet early intervention via predictive modeling remains an open challenge. This challenge is compounded by the temporal dynamics of user interactions and the sparsity of such interactions in real-world social networks, making reliable modeling difficult. Current methods predominantly focus on detecting cyberbullying after it occurs through user content and profiles, while overlooking the temporal patterns and struggling when social interaction data is limited. We propose TemSoGraph, a unified temporal social graph learning model for cyberbullying detection and prediction. The model leverages a temporal self-attention mechanism to capture time-evolving user interactions and employs joint global and local node updates to represent users with limited interactions. It further incorporates a domain adaptor that learns domain-invariant features, enhancing generalization across datasets even when labeled target data is scarce. Experiments on two real-world datasets, Instagram and Vine, show that TemSoGraph outperforms eight cyberbullying detection models in detection task and six dynamic graph neural networks in prediction task. On the prediction task, TemSoGraph achieves a recall of 97.18% on Instagram with 2.53% improvement and 93.38% on Vine with 6.25% improvement. The model supports both detection and future prediction and provides a strong benchmark for cyberbullying modeling.