Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit

Abstract

Cancer-related discussions on social media provide important spaces for information exchange and peer support, but can also expose users to misinformation with implications for prevention, screening, and treatment decisions. Existing work often treats cancer misinformation as a binary phenomenon, providing limited insight into how misinformation is expressed, engaged with, and associated with potential harm. We introduce a multi-dimensional taxonomy for characterizing cancer misinformation in Reddit discussions of breast, lung, colon, and prostate cancer. Developed through expert annotation, the taxonomy captures seven dimensions spanning misinformation presence, cancer stage, information-seeking and sharing behavior, misinformation type, risk, stance, and topical focus. We evaluate 21 large language models (LLMs) across zero- and few-shot settings and develop a cross-model agreement strategy for scaling misinformation identification to over 133K posts. Our analysis shows that misinformation is heterogeneous in both form and function: unproven and alternative treatments emerge as a prominent topic, misinformation frequently occurs within exchanges that combine information seeking and sharing, and users engage with questionable claims through both endorsement and uncertainty. We further find that classification difficulty varies substantially across dimensions, with risk assessment posing particular challenges for both human annotators and LLMs. Our taxonomy and empirical findings move beyond binary detection toward a more nuanced characterization of how cancer misinformation is produced, discussed, and encountered in online health communities.