Recent academic research has documented a growing digital privacy phenomenon termed algorithmic outing, wherein social media platforms infer and surface users' sexual orientation or gender identity before individuals have consciously disclosed it. The study, published in the journal Gender, Place & Culture and led by Dr. Justin Ellis of the University of Newcastle's School of Law and Justice, highlights how recommendation engines leverage engagement signals such as video likes, creator follows, and dwell time to build detailed user profiles. These profiles frequently trigger personalized LGBTQ+ content that users encounter outside their intended private contexts. Drawing on in-depth interviews with twenty participants aged eighteen to sixty, the research examines how queer adults navigate these digital inferences across hybrid spaces where online activity intersects with physical environments like public transit, cafes, and workplaces. Participants reported that while early algorithmic recognition of their identity provided a sense of validation and community belonging, it simultaneously introduced significant safety concerns. Algorithms operate without contextual awareness or risk assessment, meaning sensitive content can surface unpredictably during vulnerable moments. Many respondents described adopting compensatory behaviors, including activating strict privacy settings, maintaining secondary accounts, and browsing passively to avoid unwanted visibility or harassment. The findings underscore a critical gap in current platform design. Digital systems are increasingly adept at classifying users based on behavioral data, yet they lack mechanisms to align disclosure timing with user readiness or environmental safety. Dr. Ellis emphasized that algorithmic outing demonstrates how deeply embedded these systems are in daily life, noting that platforms which actively identify user demographics must also assume responsibility for how and where that information becomes visible. The research community and study participants alike are urging technology companies to implement transparency protocols, robust consent frameworks, and privacy-by-design architectures. Practical solutions proposed include explicit opt-in mechanisms for sensitive content categorization, streamlined emergency filters to rapidly clear potentially compromising material, and algorithmic audits to mitigate bias in identity classification. As social media platforms continue refining predictive engagement models, the tension between personalized recommendation and user agency intensifies. The study concludes that safeguarding identity disclosure requires a shift from reactive privacy tools to proactive platform accountability. Technology providers must recognize that algorithmic inference carries real-world consequences, particularly for marginalized communities navigating public and semi-public spaces. Addressing these risks through intentional design and ethical data governance will be essential to preserving user autonomy and digital safety in an increasingly personalized online landscape.