  This paper describes the work of the Data Science for Digital Health (DS4DH)
group at the TREC Health Misinformation Track 2021. The TREC Health
Misinformation track focused on the development of retrieval methods that
provide relevant, correct and credible information for health related searches
on the Web. In our methodology, we used a two-step ranking approach that
includes i) a standard retrieval phase, based on BM25 model, and ii) a
re-ranking phase, with a pipeline of models focused on the usefulness,
supportiveness and credibility dimensions of the retrieved documents. To
estimate the usefulness, we classified the initial rank list using pre-trained
language models based on the transformers architecture fine-tuned on the MS
MARCO corpus. To assess the supportiveness, we utilized BERT-based models
fine-tuned on scientific and Wikipedia corpora. Finally, to evaluate the
credibility of the documents, we employed a random forest model trained on the
Microsoft Credibility dataset combined with a list of credible sites. The
resulting ranked lists were then combined using the Reciprocal Rank Fusion
algorithm to obtain the final list of useful, supporting and credible
documents. Our approach achieved competitive results, being top-2 in the
compatibility measurement for the automatic runs. Our findings suggest that
integrating automatic ranking models created for each information quality
dimension with transfer learning can increase the effectiveness of
health-related information retrieval.
