Health Misinformation Detection

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9 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Privacy-Preserving Text Inference for Health-Misinformation Classification and Semantic Retrieval
Oct 14, 2026Jasleen Kaur, Chloé Cachet, Koray Karabina +2Health Misinformation Detection Privacy-Preserving ML
National Research Council Canada, Ottawa, Canada · Florida Atlantic University, Boca Raton, USA
Does Neural Complexity Improve Health Misinformation Detection? A Leakage-Controlled Cross-Corpus Benchmark
Oct 3, 2026Mkululi SIKOSANAHealth Misinformation Detection Fake News Detection
Department of Computing and Mathematics, Manchester Metropolitan University Manchester M15 6BH, United Kingdom
Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos
Sep 25, 2026Yuya Wake, Sho Tsugawa, Toshiyuki AmagasaHealth Misinformation Detection Fake News Detection
University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation
Aug 31, 2026Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun AjaoFake News Detection Automated Fact-Checking
Department Of Computing And Mathematics, Manchester Metropolitan University, Manchester, UK
BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter
Aug 16, 2026Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun AjaoHealth Misinformation Detection Topic Modeling
Department Of Computing And Mathematics, Manchester Metropolitan University, Manchester, UK
An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verification
Aug 3, 2026Isah M. Bukar, Bala Mairiga Abduljalil, Bashir Saleh Maina +1Retrieval-Augmented Generation Fake News Detection
Department of Computer Science, Yobe State University, Damaturu, Nigeria · Yobe AI Research Laboratory (YAIR Lab), Yobe State University, Damaturu, Nigeria · Center for AI & Big Data, Novosibirsk State University, Novosibirsk, Russian Federation +1
Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)
Jul 14, 2026Farnaz Farid, Raihan Alam, Al Al-Areqi +5Small Language Models Automated Fact-Checking
School of Social Sciences, Western Sydney University · Microsoft · School of Computer, Data and Mathematical Sciences, Western Sydney University +4
When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation
Apr 23, 2026Anamta Khan, Ratna Kandala, Deepti +2LLM Prompting Automated Fact-Checking
University of Michigan · University of Kansas · IIT Jodhpur
WhatsApp Vaccine Discourse (WhaVax): An Expert-Annotated Dataset and Benchmark for Health Misinformation Detection
Mar 25, 2026Jônatas H. dos Santos, Julio C. S. Reis, Philipe Melo +9Health Misinformation Detection Fake News Detection
Universidade Federal de Minas Gerais (UFMG), Brasil · Universidade Federal de Viçosa (UFV), Brasil

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Privacy-Preserving Text Inference for Health-Misinformation Classification and Semantic Retrieval

Jasleen Kaur, Chloé Cachet, Koray Karabina, Jean Belo Klamti+1

National Research Council Canada, Ottawa, Canada · Florida Atlantic University, Boca Raton, USA

Health Misinformation Detection Privacy-Preserving ML Semantic Search Automated Fact-Checking

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Health-misinformation analysis presents a dual challenge: misleading claims can affect public well-being, while systems designed to detect them may expose sensitive user-submitted text. This motivates privacy-preserving inference for public-health applications in which user queries may contain confidential medical information and trained models and reference databases are maintained on organizational servers. We design a privacy-preserving text inference framework based on CKKS homomorphic encryption for health-misinformation classification and semantic retrieval. The classification pipeline fuses BioBERT embeddings, PCA-reduced MPNet embeddings, and SVD-reduced TF-IDF features, while the retrieval pipeline combines MPNet embeddings with ISCC-SCT semantic fingerprints through late fusion. We evaluate predictive performance and benchmark plaintext versus ciphertext-based execution. The hybrid classifier achieved an accuracy of 0.971 and a macro-F1 score of 0.950, with identical reported metrics under plaintext and ciphertext-based inference. The hybrid late-fusion similarity model achieved a Pearson correlation of 0.8691 with human sentence-pair similarity scores on STS-B. For ciphertext-based retrieval, we evaluated naive and optimized implementations. The optimized implementation uses CKKS single instruction, multiple data (SIMD) packing to compute and return multiple similarity scores together, reducing single-query latency over 7,128 indexed items from 222.028 to 5.168 seconds and communication overhead from 6.11 GB to 2.13 MB. These findings demonstrate the technical feasibility of privacy-preserving text inference for health-misinformation applications and characterize its predictive utility and efficiency trade-offs.