Susceptibility to health misinformation is determined by numerous technology-level and user-level factors. While existing IS research has identified individual antecedents of misinformation susceptibility, there remains a critical gap in understanding how these factors interact to shape users’ ability to discern false health claims. This research-in-progress proposes an experimental study integrating eye tracking, facial expression analysis, think-aloud protocols, and survey data to capture user reactions to social media posts containing accurate and misleading health information. By applying Qualitative Comparative Analysis (QCA), we aim to identify configurations of conditions that lead to accurate or inaccurate detection of health misinformation, ultimately developing user profiles that categorize individuals based on their vulnerability to deception. These insights will provide a foundation for designing targeted interventions to mitigate risks associated with health misinformation belief.