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Improving online health search via multi-dimensional information quality models based on deep learning: a retrospective study
ABSTRACT
Background:
The presence of widespread misinformation in Web resources and the limited quality control provided by search engines can lead to serious implications for individuals seeking health advice.
Objective:
We aimed to investigate a multi-dimensional information quality assessment model based on deep learning to enhance the reliability of online healthcare information search results.
Methods:
In this retrospective study, we simulated online health information search scenarios with a topic set of 35 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 inferred the usefulness, supportiveness, and credibility quality dimensions of the retrieved documents for a given search query.
Results:
The usefulness model provided the largest distinction between help and harm compatibility documents with a difference of 0.053. The supportiveness model achieved the best harm compatibility (0.024), while the combination of usefulness, supportiveness, and credibility models achieved the best help and harm compatibility on helpful topics.
Conclusions:
Our results suggest that integrating automatic ranking models created for specific information quality dimensions can increase the effectiveness of health-related information retrieval for decision-making.
Citation
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Copyright
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