  Abuse in its various forms, including physical, psychological, verbal,
sexual, financial, and cultural, has a negative impact on mental health.
However, there are limited studies on applying natural language processing
(NLP) in this field in Vietnam. Therefore, we aim to contribute by building a
human-annotated Vietnamese dataset for detecting abusive content in Vietnamese
narrative texts. We sourced these texts from VnExpress, Vietnam's popular
online newspaper, where readers often share stories containing abusive content.
Identifying and categorizing abusive spans in these texts posed significant
challenges during dataset creation, but it also motivated our research. We
experimented with lightweight baseline models by freezing PhoBERT and
XLM-RoBERTa and using their hidden states in a BiLSTM to assess the complexity
of the dataset. According to our experimental results, PhoBERT outperforms
other models in both labeled and unlabeled abusive span detection tasks. These
results indicate that it has the potential for future improvements.
