SugarTextNet Targets Sugar-Dating Content Detection on Social Media
An arXiv preprint proposes SugarTextNet, a model trained to identify explicit and euphemistic sugar-dating posts on Sina Weibo. The reported gains come from one small, platform-specific dataset and do not establish that the model is ready for moderation decisions.
SugarTextNet is a research preprint proposing a specialized transformer model to identify sugar-dating-related posts on social media, where euphemisms and severe class imbalance make simple keyword filters unreliable. The authors evaluated the approach on 3,067 manually annotated Chinese-language posts collected from Sina Weibo. The architecture combines a pretrained transformer encoder, an attention-based cue extractor and a contextual phrase encoder to capture explicit and indirect language.
A Context-Aware Focal Loss function gives more weight to difficult minority-class examples, addressing the fact that relevant posts are rare compared with ordinary content. The authors report that SugarTextNet outperformed traditional machine-learning models, deep-learning baselines and large language models across multiple evaluation metrics. The researchers also removed individual components in ablation tests to measure how much each part contributed to performance.
A specialized model may recognize coded language that broad filters miss. It may also flag news, education or consensual discussion, especially as vocabulary changes. Before enforcement use, the approach would need broader testing, human review and a workable appeals process. The work is an arXiv preprint and had not necessarily completed peer review. The dataset is small, Chinese-language and platform-specific; the abstract does not establish performance across regions, dialects or changing euphemisms.