Online health search via multi-dimensional information quality assessment based on deep language models

Abstract

AbstractBackgroundWidespread misinformation in Web resources can lead to serious implications for individuals seeking health advice. Despite that, information retrieval models are often focused only on the query-document relevance dimension to rank results.ObjectiveWe investigate a multi-dimensional information quality retrieval model based on deep learning to enhance the effectiveness of online healthcare information search results.MethodsIn this study, we simulated online health information search scenarios with a topic set of 32 different health-related inquiries and a corpus containing one billion Web documents from the April 2019 snapshot of Common Crawl. Using state-of-the-art pre-trained language models, we assessed the quality of the retrieved documents according to their usefulness, supportiveness, and credibility dimensions for a given search query on 6,030 human-annotated query-document pairs. We evaluated this approach using transfer learning and more specific domain adaptation techniques.ResultsIn the transfer learning setting, the usefulness model provided the largest distinction between help- and harm-compatible documents with a difference of +5.6%, leading to a majority of helpful documents in the top-10 retrieved. The supportiveness model achieved the best harm compatibility (+2.4%), while the combination of usefulness, supportiveness, and credibility models achieved the largest distinction between help- and harm-compatibility on helpful topics (+16.9%). In the domain adaptation setting, the linear combination of different models showed robust performance with help-harm compatibility above +4.4% for all dimensions and going as high as +6.8%.ConclusionsThese results suggest that integrating automatic ranking models created for specific information quality dimensions can increase the effectiveness of health-related information retrieval. Thus, our approach could be used to enhance searches made by individuals seeking online health information.