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How Misinformation Density Affects Health Information Search

Published: 25 April 2022 Publication History
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    Search engine results can include misinformation that is inaccurate, misleading, or even harmful. But people may not recognize or realize false information results when searching online. We suspect that the percentage of misinformation search results (misinformation density) may influence people’s search activities, learning outcomes, and search experience. We conducted a zoom-mediated “lab” user study to examine this matter. The experiment used a between-subjects design. We asked 60 participants to finish two health information search tasks using search engines with High, Medium, or Low misinformation density levels. To create these experimental settings, we trained task-dependent text classifiers to manipulate the number of correct and misinformation results displayed on SERPs. We collected participants’ search activities, responses to pre-task and post-task surveys, and answers to task-related factual questions before and after searching.
    Our results indicate that search result misinformation density strongly affects users’ search behavior. High misinformation density made people search more frequently, use longer queries, and click on more results. However, such increased search activities did not lead to better search outcomes. Participants using the High misinformation density search engine answered factual questions less accurately and learned very limitedly from a search session than the two other systems. Moreover, participants in systems with a balanced amount of correct and misinformation results (Medium) could learn factual knowledge as effectively as others in a system with little misinformation (Low). Surprisingly, participants using different misinformation density systems did not rate their perceived goodness of search systems with significant differences, indicating that search engine misinformation may adversely but imperceptibly affect people and society. Our findings have disclosed the effects of misinformation density on health information search and offered insights to improve online health information search.

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    Cited By

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    • (2024)MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation DetectionProceedings of the ACM on Web Conference 202410.1145/3589334.3648152(4653-4663)Online publication date: 13-May-2024
    • (2024)Misinformation does not reduce trust in accurate search results, but warning banners may backfireScientific Reports10.1038/s41598-024-61645-814:1Online publication date: 14-May-2024
    • (2022)Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531812(2099-2104)Online publication date: 6-Jul-2022

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          cover image ACM Conferences
          WWW '22: Proceedings of the ACM Web Conference 2022
          April 2022
          3764 pages
          ISBN:9781450390965
          DOI:10.1145/3485447
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          Published: 25 April 2022

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          1. health information search
          2. misinformation
          3. search as learning

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          April 25 - 29, 2022
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          • (2024)MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation DetectionProceedings of the ACM on Web Conference 202410.1145/3589334.3648152(4653-4663)Online publication date: 13-May-2024
          • (2024)Misinformation does not reduce trust in accurate search results, but warning banners may backfireScientific Reports10.1038/s41598-024-61645-814:1Online publication date: 14-May-2024
          • (2022)Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531812(2099-2104)Online publication date: 6-Jul-2022

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