Vera: Prediction Techniques for Reducing Harmful Misinformation in Consumer Health Search
Ronak Pradeep, Xueguang Ma, Rodrigo Nogueira, Jimmy Lin
Abstract
The COVID-19 pandemic has brought about a proliferation of harmful news articles online, with sources lacking credibility and misrepresenting scientific facts. Misinformation has real consequences for consumer health search, i.e., users searching for health information. In the context of multi-stage ranking architectures, there has been little work exploring whether they prioritize correct and credible information over misinformation. We find that, indeed, training models on standard relevance ranking datasets like MS MARCO passage---which have been curated to contain mostly credible information---yields models that might also promote harmful misinformation. To rectify this, we propose a label prediction technique that can separate helpful from harmful content. Our design leverages pretrained sequence-to-sequence transformer models for both relevance ranking and label prediction. Evaluated at the TREC 2020 Health Misinformation Track, our techniques represent the top-ranked system: Our best submitted run was 19.2 points higher than the second-best run based on the primary metric, a 68% relative improvement. Additional post-hoc experiments show that we can boost effectiveness by another 3.5 points.