It has long been known social media uses have led to changes in privacy practices. These changes tend to challenge existing societal norms about sharing and being seen, ultimately impacting individuals’ expectations on and practices of privacy. Yet, little is known about how platforms’ privacy settings and users’ awareness of them influence which personal narratives (e.g., harrowing, heartening) are more likely to be posted online in relation to genetic conditions. 

Moderation policies play a significant role in shaping overall user practices. By relying on the work of human reviewers and automatic software, social media companies moderate user-generated content to remove offensive and illegal content and to shape their environment to attract new users, advertisers and partners. However, moderation is often experienced at an intimate level. For instance, women who saw their breastfeeding photos removed from Facebook in 2007 or fought for their post-mastectomy selfies to be allowed on the platform in 2015 felt personally attacked by the platform’s policy on nudity and obscene content. In both cases, the controversy prompted those involved to join forces and protest. 

While moderation determines what can be posted on a platform, algorithmic recommendation determines what can be seen on it, that is, it determines relevance - and visibility - of both content and users. Algorithmic recommendation systems follow data patterns: they match users and content on the basis of engagement metrics: the more a user - and users similar to them – shows interest in a type of content, the more they will be exposed to similar content. Algorithmic recommendation systems work on the backstage, making it difficult for users to even see their content curation.