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This paper describes the work of the Data Science for Digital Health (DS4DH)group at the TREC Health Misinformation Track 2021. The TREC HealthMisinformation track focused on the development of retrieval methods thatprovide relevant, correct and credible information for health related searcheson the Web. In our methodology, we used a two-step ranking approach thatincludes i) a standard retrieval phase, based on BM25 model, and ii) are-ranking phase, with a pipeline of models focused on the usefulness,supportiveness and credibility dimensions of the retrieved documents. Toestimate the usefulness, we classified the initial rank list using pre-trainedlanguage models based on the transformers architecture fine-tuned on the MSMARCO corpus. To assess the supportiveness, we utilized BERT-based modelsfine-tuned on scientific and Wikipedia corpora. Finally, to evaluate thecredibility of the documents, we employed a random forest model trained on theMicrosoft Credibility dataset combined with a list of credible sites. Theresulting ranked lists were then combined using the Reciprocal Rank Fusionalgorithm to obtain the final list of useful, supporting and credibledocuments. Our approach achieved competitive results, being top-2 in thecompatibility measurement for the automatic runs. Our findings suggest thatintegrating automatic ranking models created for each information qualitydimension with transfer learning can increase the effectiveness ofhealth-related information retrieval.

DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion