It’s not Sexually Suggestive; It’s Educative | Separating Sex Education from Suggestive Content on T ik T ok videos (2023)

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.

## introduction in short-form videos such as in tiktok, accurately identifying sexually suggestive and sex education content amidst a sea of diverse video types poses a significant challenge. in this paper, we delve into this problem, focusing specifically on tiktok, the most downloaded app in 2022, which has a sub stantial user base of early adolescents and young individuals (10-19: 32.5%, 20-29: 29.5%) 1 the distinction between suggestive videos and virtual sex education holds crucial significance on multiple fronts. adolescent sex education in the united states is delivered in a fragmented and often inadequate system, which has long been the 1 https://wallaroomedia.com/blog/social-media/ tiktok-statistics/ figure 1 : two screenshots from videos in the dataset. on the left, nyko (@kingnyko2022) addresses a question about his gender transition. the right is from a sexually suggestive video. unfortunately, efforts to moderate explicit content had unintended consequences, as studies have demonstrated the misidentification of non-explicit content due to flawed algorithms and filtering tech niques peters, 2020. in addition to the above issue, video/video creators (referred to as creators from now on) may also be susceptible to mass reporting. creators from marginalized communities, partic ularly those within the lgbtqia+ community, face heightened risks of having their educational content wrongfully flagged or removed foot_0 . the classification of sexually suggestive and sex education videos presents a complex task, as demonstrated by the examples shown in in example 1, we see that p*n*s illustration is not suggestive, while the video with a man holding a pumpkin in example 2 is suggestive. when we look at the transcripts, we see that in example 3, the creator is talking about myths around p*n*s sizes for pleasurable sex, and in example 4, the audio is suggestive. considering these complexities, accurately categorizing sexually suggestive and sex education videos necessitates a nuanced understanding of contextual cues, subjectivity, evolving language, and robust algorithmic solutions. the contributions of the paper are as follows: ## experimental setups in this section, we evaluate the performance of pretrained transformer-based models on the sextok dataset to assess its significance. the experiments are divided into two subsections: text classification using video transcripts and video classification. for both transformer-based setups, we utilized models downloaded from hugging face transformers @xcite , initializing them with three random numbers. details on hyperparameters are in appendix c. the reported results are the average of three runs. to assess the performance, we employed four sets of metrics: (1) accuracy, (2) micro precision, recall, and f1 (excluding others as a negative class from the scores), (3) macro precision, recall, and f1, and (4) overall f1 for each class. ## results and error analysis the average performance and standard deviation of the models are presented in tables 4 and 5 . based on these results, we draw the following observations: to further understand the hard examples for the model, we manually categorized the errors in both text and video classification experiment setups. we analysed 54 errors in text classification model. if more than one option was applicable, the video was counted in both: (a) audio unrelated to class label (50.00%): the audio in these videos ## discussion the results highlight the complexity of accurately identifying sexually suggestive and educative videos on platforms like tiktok. while the results indicate that text analysis can contribute to detecting educative videos, music clips unrelated to the video topic are commonly used, making reliance on transcription alone insufficient. while existing work in pornographic content detection primarily focuses on visual analysis, our results indicate the need for a multi-modal approach since detecting sexual content requires a more comprehensive understanding encompassing multiple senses, including audio, speech, and text. addressing these challenges is crucial for developing effective content moderation systems, ensuring appropriate access to sex education, and creating a safer and more inclusive online environment. it is also crucial to be mindful of potential gender expression bias commonly found in visual datasets @xcite . moreover, for tasks like this, developing scalable solutions suitable for large-scale systems with millions of users is crucial for effective implementation. further exploration and investigation of these aspects are left for future research and development. ## conclusion this paper introduces a novel task of identifying sexually suggestive and sex-educative videos and presents sextok, a multi-modal dataset for this purpose. the dataset includes video links labeled for sexual suggestiveness, sex-educational content, and an other category, along with gender expression and audio transcription. the results highlight the challenging and multi-modal nature of the task and suggest that while the dataset is meaningful and the task is learnable, it remains a challenging problem that deserves future research. this work contributes to promoting online safety and a balanced digital environment.