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

It is not Sexually Suggestive, It is Educative. Separating Sex Education from Suggestive Content on TikTok Videos