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This paper addresses the problem of detecting CTs in social media posts with an emphasis on the resource\u2010constrained scenarios characterized by the scarcity of labelled datasets and expert annotations, and the lack of computational budget for large\u2010scale LLM inference. To address these challenges, we investigate resource\u2010efficient methods for CT detection across multiple epidemics. We construct a novel dataset of CT\u2010labelled social media posts covering four major epidemics from the past decade: Ebola, Zika, COVID\u201019 and Monkeypox. We conduct extensive experiments addressing four research questions: (1) the performance of BERT\u2010like models on individual epidemics, (2) the ability to transfer knowledge from past epidemics to new ones, (3) the efficacy of zero\u2010shot classification using Large Language Models (LLMs) and (4) the feasibility of training BERT\u2010like models on LLM\u2010labelled datasets. Our findings indicate that BERT\u2010like models exhibit highly variable performance across epidemics. Transfer learning from prior epidemics can be effective and their performance can be improved with the number of prior datasets. Zero\u2010shot LLM classifiers, including ensemble methods, achieve performance that matches or surpasses that of fine\u2010tuned BERT\u2010like models. Finally, we demonstrate that BERT\u2010like models trained on LLM\u2010labelled datasets achieve results close to the models trained on expert\u2010annotated data, offering a practical alternative when expert labelling is infeasible. While automated methods can be useful for data analysis, we caution against automatization of content filtering due to the inherent difficulty of CT detection and the potential biases of language models.<\/jats:p>","DOI":"10.1111\/exsy.70360","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T10:45:17Z","timestamp":1784285117000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Past Outbreaks to Future Threats: Detecting Medical Conspiracy Theories With\n                    <scp>LLMs<\/scp>\n                    and Limited Labels"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5037-2203","authenticated-orcid":false,"given":"Ipek Baris","family":"Schlicht","sequence":"first","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia  Valencia Spain"},{"name":"DW Innovation  Bonn Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damir","family":"Koren\u010di\u0107","sequence":"additional","affiliation":[{"name":"Division of Computing and Data Science Ru\u0111er Bo\u0161kovi\u0107 Institute  Zagreb Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berta","family":"Chulvi","sequence":"additional","affiliation":[{"name":"Universitat de Val\u00e8ncia  Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucie","family":"Flek","sequence":"additional","affiliation":[{"name":"Bonn\u2010Aachen International Center for IT University of Bonn  Bonn Germany"},{"name":"Lamarr Institute for Machine Learning and Artificial Intelligence  Dortmund Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Rosso","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia  Valencia Spain"},{"name":"ValgrAI Valencian Graduate School and Research Network of Artificial Intelligence  Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"e_1_2_16_2_1","first-page":"611","volume-title":"Findings of the ACL: EMNLP 2021","author":"Alam F.","year":"2021"},{"key":"e_1_2_16_3_1","first-page":"67","volume-title":"Proceedings of the 18th Conference of the EACL (Volume 1: Long Papers)","author":"Balloccu S.","year":"2024"},{"key":"e_1_2_16_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0007791"},{"key":"e_1_2_16_5_1","first-page":"1877","volume-title":"NIPS","author":"Brown T.","year":"2020"},{"key":"e_1_2_16_6_1","unstructured":"Bucher M. 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