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

• URL: http://arxiv.org/abs/2607.12383v1
• Date: Tue, 14 Jul 2026 05:55:44 GMT
• Title: Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit
• Abstract summary: We introduce a multi-dimensional taxonomy for characterizing cancer misinformation in Reddit discussions of breast, lung, colon, and prostate cancer.<n>We evaluate multiple large language models (LLMs) for scalable misinformation annotation and analyze cancer misinformation across Reddit communities.<n>Our results show that cancer-related misinformation constitutes approximately 6% of Reddit cancer discussions, with substantial variation across communities and misinformation topics.
• Score: 2.045935105132131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer-related discussions on social media provide an important space for information exchange and peer support, but also facilitate the spread of misinformation that may influence prevention, screening, and treatment decisions. Existing research on cancer misinformation often relies on narrow definitions, small-scale datasets, or binary labeling frameworks. We introduce a multi-dimensional taxonomy for characterizing cancer misinformation in Reddit discussions of breast, lung, colon, and prostate cancer. The taxonomy captures seven dimensions, including misinformation presence, information type, risk level, stance, and topical focus. Using expert-annotated data, we evaluate multiple large language models (LLMs) for scalable misinformation annotation and analyze cancer misinformation across Reddit communities. Our results show that cancer-related misinformation constitutes approximately 6\% of Reddit cancer discussions, with substantial variation across communities and misinformation topics. Few-shot prompting substantially improves classification performance, particularly for nuanced taxonomy dimensions. We additionally identify recurring misinformation narratives centered on unsupported treatments, distrust of conventional medicine, and misleading claims about diagnosis and screening. Our taxonomy, dataset, and findings provide a foundation for multi-dimensional modeling of online cancer misinformation.

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