  In this paper, we describe our participation in the TREC Health
Misinformation Track 2020. We submitted $11$ runs to the Total Recall Task and
13 runs to the Ad Hoc task. Our approach consists of 3 steps: (1) we create an
initial run with BM25 and RM3; (2) we estimate credibility and misinformation
scores for the documents in the initial run; (3) we merge the relevance,
credibility and misinformation scores to re-rank documents in the initial run.
To estimate credibility scores, we implement a classifier which exploits
features based on the content and the popularity of a document. To compute the
misinformation score, we apply a stance detection approach with a pretrained
Transformer language model. Finally, we use different approaches to merge
scores: weighted average, the distance among score vectors and rank fusion.
