@inproceedings{pessianzadeh-etal-2026-beyond,
title = "Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on {R}eddit",
author = "Pessianzadeh, Aria and
Jamie, Pooriya and
Sultana, Naima and
Himmelstein, Gracie and
Zektser, Yuliya and
Ganz, Patricia A. and
Hosseinmardi, Homa and
Ghasemian, Amir and
Rezapour, Rezvaneh",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1896/",
pages = "40392--40419",
ISBN = "979-8-89176-462-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit
%A Pessianzadeh, Aria
%A Jamie, Pooriya
%A Sultana, Naima
%A Himmelstein, Gracie
%A Zektser, Yuliya
%A Ganz, Patricia A.
%A Hosseinmardi, Homa
%A Ghasemian, Amir
%A Rezapour, Rezvaneh
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F pessianzadeh-etal-2026-beyond
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.1896/
%P 40392-40419
Markdown (Informal)
[Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit](https://aclanthology.org/2026.findings-emnlp.1896/) (Pessianzadeh et al., Findings 2026)
ACL
- Aria Pessianzadeh, Pooriya Jamie, Naima Sultana, Gracie Himmelstein, Yuliya Zektser, Patricia A. Ganz, Homa Hosseinmardi, Amir Ghasemian, and Rezvaneh Rezapour. 2026. Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 40392–40419, Budapest, Hungary. Association for Computational Linguistics.