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.
Publisher
Cold Spring Harbor Laboratory
Cited by
3 articles.
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