Medication nonadherence (MNA) refers to the behavior when patients do not fill prescriptions. To take proactive measures and prevent harmful outcomes, the stakeholders need to understand patientsâ€™ reasons for MNA. Current studies attempt to provide one-size-fits-all solutions to the â€œaverage patientsâ€� and utilize survey or experiment design with small sample sizes to obtain a snapshot of this issue. To address these issues, we develop a semantically enhanced deep learning approach to detecting patient and drug-specific reasons for MNA using health social media data. Our model reached a precision of 86.43%, a recall of 92.53%, and an F1- score of 89.38%. This study contributes to information systems research by designing a deep-learning-based framework for detecting tailored reasons for MNA in real time. The framework is generalizable to understand motivations of various human behaviors. We also contribute to healthcare IT by discovering previously unknown MNA reasons from online health IT platforms.