Fooled by the Algorithm: Self-Censorship and Tabooization of S€K$Ual Education on Social Media

The reliance on social media for sexual education is constrained by self-censorship and perceived algorithmic penalties, notably shadowbanning. To navigate platform moderation, creators frequently encode terms like “org@sm” or “s€x”, inadvertently reinforcing societal taboos and limiting open discussions. However, sex and relationship education are an integral part of education, and the need for education in this area has been a subject of discussion among experts for a long time. This study examines autocensorship in digital sexual education, assessing its perceived necessity and ineffectiveness in evading algorithms. It analyzes platform policies’ roles in shaping content creators’ behaviours and the resulting societal impacts of restricted access to clear educational content. Additionally, the paper evaluates whether social media-based sexual education aligns with established expert knowledge, highlighting potential gaps in credibility and accuracy. By exploring the interplay between content creators, algorithms, and audience perceptions, the research emphasizes the need for greater transparency and accountability on social media platforms, advocating for policy changes that differentiate educational material from harmful content to foster more informed and destigmatized digital discourse on sexuality. The study is based on the framework of the digital transformation of media, which influences both how content is created and how people perceive it – especially on social media. The study thus offers a concrete view of these changes through the example of digital sex education.