Abstract

The phenomenon of self-diagnosis regarding mental health disorders has escalated significantly due to the massive influx of educational content aggressively personalized by social media algorithms. The novelty of this study lies in identifying the phenomenon of 'digital identity fusion,' where social media algorithms do more than validate symptoms; they reconstruct mental health labels into instruments for social navigation and self-aesthetics that paradoxically diminish the user’s self-efficacy. This study aims to elucidate how algorithmic mechanisms contribute to reshaping the perception of mental illness, shifting the paradigm from a clinical diagnosis to a central facet of the user’s self-identity. Methods: Employing a qualitative phenomenological approach (Interpretative Phenomenological Analysis), this research involved three participants (n = 3) selected through purposive sampling to represent different social pressure spectrums: a university student, a private employee, and a creative freelancer. Primary data were collected through in-depth interviews focusing on the history of algorithmic exposure and the motivations behind self-diagnostic behavior. Results: The results indicate that algorithms create echo chambers that trigger confirmation bias, leading individuals to adopt labels such as ADHD or Anxiety as self-defense mechanisms against external pressures. The findings reveal an identity fusion in which mental health labels are used as aesthetics or personal branding to construct a unique or "authentic" self-narrative within digital spaces. Collectively, these interactions with algorithms have diminished participants' self-efficacy, as individuals feel bound to a permanent "disease identity". Conclusion: The study concludes that self-diagnosis has shifted from a mere search for medical information toward the formation of a core identity used for social navigation. Consequently, future psychological interventions must integrate algorithmic literacy to restore individual personal agency.