  We introduce SexTok, a multi-modal dataset composed of TikTok videos labeled
as sexually suggestive (from the annotator's point of view), sex-educational
content, or neither. Such a dataset is necessary to address the challenge of
distinguishing between sexually suggestive content and virtual sex education
videos on TikTok. Children's exposure to sexually suggestive videos has been
shown to have adversarial effects on their development. Meanwhile, virtual sex
education, especially on subjects that are more relevant to the LGBTQIA+
community, is very valuable. The platform's current system removes or penalizes
some of both types of videos, even though they serve different purposes. Our
dataset contains video URLs, and it is also audio transcribed. To validate its
importance, we explore two transformer-based models for classifying the videos.
Our preliminary results suggest that the task of distinguishing between these
types of videos is learnable but challenging. These experiments suggest that
this dataset is meaningful and invites further study on the subject.
