Misinformation on social media platforms has become a pervasive problem in recent years, with the potential to have a significant negative impact on society. One of the most effective countermeasures against misinformation is social correction, which refers to attempts to correct the source of misinformation. Social media platforms’ misinformation reporting function (MRF) can be regarded as a form of social correction. However, current research on MRFs is limited, and there is a need to understand better the factors that affect users’ intentions to use them. In this study, we aim to address this gap by integrating the expectation confirmation model (ECM) and protection motivation theory (PMT) to develop a research model that explains users’ intentions to use MRFs. ECM posits that users’ confirmation or disconfirmation of expectations determines their satisfaction with a new product or feature. PMT, on the other hand, emphasizes the role of threat appraisal and response appraisal in motivating behavior. The proposed research model hypothesizes that users’ intentions to use MRFs can be affected by their threat appraisal of misinformation (including perceived severity and susceptibility), their response appraisal of MRFs (including self-efficacy, response efficacy, and response costs), and their confirmation or disconfirmation of expectations about MRFs. We conducted a quantitative study using a questionnaire survey to test the proposed research model. The questionnaire measured users’ threat appraisal, response appraisal, confirmation or disconfirmation of expectations, and intentions to use MRFs. The data collected from the survey were analyzed using structural equation modeling. The findings of this study will have important implications for both theory and practice. Theoretically, the study will contribute to a better understanding of the factors affecting users’ intentions to use MRFs. From a practical perspective, the study will provide valuable insights for social media platforms on designing and promoting MRFs that are effective in reducing the spread of misinformation. We explore the use of social media misinformation reporting capabilities. If the data collected from the MRF can be treated as training data for machine learning models and sensor-based misinformation detection systems, it will be possible to deal more effectively with the phenomenon of misinformation in social media through natural language processing and image analysis.