UPV at TREC Health Misinformation Track 2021 Ranking with SBERT and Quality Estimators
This work addresses health misinformation for search engine users, but it is incremental as it applies existing methods to a specific track.
The paper tackled the problem of health misinformation in search engines by developing a retrieval and re-ranking system for the TREC Health Misinformation Track 2021, achieving results through a combination of BM25, semantic search, quality assessment, and score fusion.
Health misinformation on search engines is a significant problem that could negatively affect individuals or public health. To mitigate the problem, TREC organizes a health misinformation track. This paper presents our submissions to this track. We use a BM25 and a domain-specific semantic search engine for retrieving initial documents. Later, we examine a health news schema for quality assessment and apply it to re-rank documents. We merge the scores from the different components by using reciprocal rank fusion. Finally, we discuss the results and conclude with future works.