Creepy, Invasive, and Exploitative Algorithms: A CPM Analysis of Users’ Privacy Breakdowns and Recalibration Practices With Social Media Algorithms

Social media content filtering algorithms can both provide desired personalized content and ads for users. However, sometimes these recommendations can resemble individual private information. How might users navigate these experiences to best manage their private information? The present exploratory study utilizes the rules- and systems-based framework of communication privacy management (CPM) theory to explore social media users’ (N = 636) experiences of privacy breakdowns with social media algorithms and investigates what users do in response to said breakdowns. These responses were refined using content analysis and divided into different categories of privacy breakdowns and recalibration strategies. Implications for future research surrounding human-machine communication privacy management are discussed in light of our findings.