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    <abstract>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 (Collins et al. 2017). Meanwhile, virtual sex education, especially on subjects that are more relevant to the LGBTQIA+ community, is very valuable (Mitchell et al. 2014). The platform’s current system removes/punishes 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.</abstract>
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%0 Conference Proceedings
%T It’s not Sexually Suggestive; It’s Educative | Separating Sex Education from Suggestive Content on TikTok videos
%A George, Enfa
%A Surdeanu, Mihai
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F george-surdeanu-2023-sexually
%X 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 (Collins et al. 2017). Meanwhile, virtual sex education, especially on subjects that are more relevant to the LGBTQIA+ community, is very valuable (Mitchell et al. 2014). The platform’s current system removes/punishes 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.
%R 10.18653/v1/2023.findings-acl.365
%U https://aclanthology.org/2023.findings-acl.365
%U https://doi.org/10.18653/v1/2023.findings-acl.365
%P 5904-5915

Markdown (Informal)

[It’s not Sexually Suggestive; It’s Educative | Separating Sex Education from Suggestive Content on TikTok videos](https://aclanthology.org/2023.findings-acl.365) (George & Surdeanu, Findings 2023)

• It’s not Sexually Suggestive; It’s Educative | Separating Sex Education from Suggestive Content on TikTok videos (George & Surdeanu, Findings 2023)

ACL

• Enfa George and Mihai Surdeanu. 2023. It’s not Sexually Suggestive; It’s Educative | Separating Sex Education from Suggestive Content on TikTok videos. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5904–5915, Toronto, Canada. Association for Computational Linguistics.