=Paper=
{{Paper
|id=Vol-3677/keynote1
|storemode=property
|title=Health misinformation detection: search challenges, annotation issues and reliability of LLMs (Keynote)
|pdfUrl=https://ceur-ws.org/Vol-3677/keynote.pdf
|volume=Vol-3677
|authors=David E. Losada
|dblpUrl=https://dblp.org/rec/conf/ecir/Losada24
}}
==Health misinformation detection: search challenges, annotation issues and reliability of LLMs (Keynote)==
<pdf width="1500px">https://ceur-ws.org/Vol-3677/keynote.pdf</pdf>
<pre>
                                Health misinformation detection: search challenges,
                                annotation issues and reliability of LLMs (Keynote)
                                David E. Losada
                                Universidad de Santiago de Compostela, Centro Singular de Investigación en Tecnoloxías Intelixentes, Spain


                                            Abstract
                                            As part of ROMCIR 2024, the 4th Workshop on Reducing Online Misinformation through Credible
                                            Information Retrieval, Prof. David E. Losada was invited to give a Keynote Speech discussing the current
                                            challenges related to the problem of health misinformation. The abstract of the speech follows.
                                                In this presentation, I will share insights from our work at CiTIUS (Centro Singular de Investigación
                                            en Tecnoloxías Intelixentes, Universidad de Santiago de Compostela, Spain) on the development of tech-
                                            nological and scientific solutions for detecting health misinformation. I will delve into the complexities
                                            of developing a multi-faceted retrieval system for misinformation detection that integrates multiple
                                            content-based features. The challenges of creating robust credibility benchmarks, given the subjective
                                            nature of credibility, will also be discussed. Lastly, I will share our recent efforts to evaluate the quality
                                            of LLMs’ responses to health-related queries.
                                                More details can be found on the ROMCIR 2024 website: https://romcir.disco.unimib.it/.

                                            Keywords
                                            Health misinformation, Credibility, Information Retrieval, Evaluations, Large Language Models




                                ROMCIR 2024: The 4th Workshop on Reducing Online Misinformation through Credible Information Retrieval (held as
                                part of ECIR 2024: the 46th European Conference on Information Retrieval), March 24, 2024, Glasgow, UK
                                Envelope-Open david.losada@usc.es (D. E. Losada)
                                GLOBE https://citius.gal/team/david-enrique-losada-carril/ (D. E. Losada)
                                Orcid 0000-0001-8823-7501 (D. E. Losada)
                                          © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).




CEUR
                  ceur-ws.org
Workshop      ISSN 1613-0073
Proceedings

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