Recommending research articles to consumers of online vaccination information
| Autor: | Harrison, Eliza, Martin, Paige, Surian, Didi, Dunn, Adam G. |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Quantitative Science Studies, 1(2):810-823 (2020) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1162/qss_a_00030 |
| Popis: | Online health communications often provide biased interpretations of evidence and have unreliable links to the source research. We tested the feasibility of a tool for matching webpages to their source evidence. From 207,538 eligible vaccination-related PubMed articles, we evaluated several approaches using 3,573 unique links to webpages from Altmetric. We evaluated methods for ranking the source articles for vaccine-related research described on webpages, comparing simple baseline feature representation and dimensionality reduction approaches to those augmented with canonical correlation analysis (CCA). Performance measures included the median rank of the correct source article; the percentage of webpages for which the source article was correctly ranked first (recall@1); and the percentage ranked within the top 50 candidate articles (recall@50). While augmenting baseline methods using CCA generally improved results, no CCA-based approach outperformed a baseline method, which ranked the correct source article first for over one quarter of webpages and in the top 50 for more than half. Tools to help people identify evidence-based sources for the content they access on vaccination-related webpages are potentially feasible and may support the prevention of bias and misrepresentation of research in news and social media. Comment: 12 pages, 5 figures, 2 tables |
| Databáze: | arXiv |
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