Misinformation reporting systems (MRSs) have been deployed on online communication platforms to crowdsource information about misinformation from public reporters. As the only communication channel between platform operators and reporters, the primary goal of MRSs is to collect misinformation details and assist platform operators in making informed decisions about combating misinformation. An MRS enables a reporter to alert the existence of misleading content and submit a report about misinformation incidents.Despite the growing importance of MRSs, current systems reveal a critical limitation, the lack of feedback to reporters. To address this limitation, the dissertation introduces feedback-integrated MRSs, including a novel GenAI-feedback module that generates adaptive, human-like responses using the GPT-4 model, one of the OpenAI’s large language models (LLMs).Through a controlled experiment, the study compares the effects of five feedback modules (GenAI, objective, prospective, scheduled, and baseline) on misinformation reporters’ intention to continue to use an MRS, perceived helpfulness of feedback, and perceived clarity of feedback. Also, the study investigates the moderating effect of prior experiences, whether a participant has previously used an MRS or received feedback.Findings indicate that the GenAI-feedback module outperforms static feedback modules in most dimensions, improving reporter engagement and perceptions of MRS usage. The results highlight the value of adaptive, personalized feedback in enhancing the functionality and reporter experience of MRSs. The study contributes to the literature on misinformation mitigation, MRS feedback design, and human–AI interaction by empirically demonstrating how feedback, especially AI-generated feedback, can improve engagement in misinformation reporting systems. Implications for future design and research directions are discussed.
