Altmetrics数据用于测量社会影响的有效性：使用Altmetric和F1000Prime数据的研究
Validity of altmetrics data for measuring societal impact: A study using data from Altmetric and F1000Prime
摘要：
测高数据能有效地用于社会影响的测量吗？目前的研究试图通过一个来自不同来源（F1000，Altmetric和基于Web of Science的内部数据库）的综合数据集（约100，000条记录）来回答这个问题。在F1000同行评审系统中，专家们为科学论文贴上特定标签，以表明一篇论文是否会引起科学界或社会其他阶层的兴趣。结果表明，如果论文的质量得到控制，带有“有益于教学”标签的论文确实比没有这个标签的论文获得更高的altmetric计数。与此同时，更高的引用计数显示，特别是有一个标签的论文是专门面向科学（“新发现”）。研究结果表明，为研究领域以外的读者量身定制的论文应该会产生社会影响。如果将测高数据用于衡量社会影响，则会出现标准化问题。在文献计量学中，引文按论文的学科领域和出版年份进行规范化。这项研究已经采取了第二个分析步骤，涉及可能的标准化的高程数据。正如结果所示，有一些特定的科学主题是广大观众特别感兴趣的。由于这些或多或少有趣的主题并没有完全反映在汤森路透的期刊集中，因此Altmetric数据的标准化不应该基于主题类别的水平，而是基于主题的水平。
英文摘要：
Can altmetric data be validly used for the measurement of societal impact? The current study seeks to answer this question with a comprehensive dataset (about 100,000 records) from very disparate sources (F1000, Altmetric, and an in-house database based on Web of Science). In the F1000 peer review system, experts attach particular tags to scientific papers which indicate whether a paper could be of interest for science or rather for other segments of society. The results show that papers with the tag "good for teaching" do achieve higher altmetric counts than papers without this tag - if the quality of the papers is controlled. At the same time, a higher citation count is shown especially by papers with a tag that is specifically scientifically oriented ("new finding"). The findings indicate that papers tailored for a readership outside the area of research should lead to societal impact. If altmetric data is to be used for the measurement of societal impact, the question arises of its normalization. In bibliometrics, citations are normalized for the papers' subject area and publication year. This study has taken a second analytic step involving a possible normalization of altmetric data. As the results show there are particular scientific topics which are of especial interest for a wide audience. Since these more or less interesting topics are not completely reflected in Thomson Reuters' journal sets, a normalization of altmetric data should not be based on the level of subject categories, but on the level of topics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
