altmetrics用于测量研究的更广泛影响的可行性：使用PLOS（altmetrics）和F1000Prime（纸质标签）数据的案例研究
Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS (altmetrics) and F1000Prime (paper tags)
摘要：
目的：虽然引用计数允许测量研究对研究本身的影响，但在测量研究对社会其他部分的影响方面的重要作用归因于altmetrics。本案例研究调查的有用性altmetrics测量研究的更广泛的影响。方法：本案例研究基本上是基于从F1000获得的论文数据集。该数据集使用PLOS（公共科学图书馆）提供的altmetrics（如Twitter计数）进行了增强。该案例研究共涉及1，082篇论文。研究结果：F1000数据集包含由专家智力分配的论文标签，可以识别论文。高程研究最有趣的标签是“有利于教学”。这个标签被分配给那些可能比专业领域的同行更广泛的读者圈感兴趣的论文。特别是在Facebook和Twitter上，人们可以期望带有此标签的论文比没有此标签的论文更常被提及。关于“有益于教学”标签，回归模型的结果能够证实这些预期：带有此标签的论文显示出比没有此标签的论文更高的Facebook和Twitter计数。这种关联在Mendeley或Figshare计数中看不到（即来自主要在科学背景下感兴趣的平台的计数）。结论：目前的研究结果表明，Facebook和Twitter，而不是Figshare或Mendeley，可以提供更广泛的读者圈感兴趣的论文的指示（而不仅仅是专业领域的同行），因此似乎对社会影响测量有用。
英文摘要：
Purpose: Whereas citation counts allow the measurement of the impact of research on research itself, an important role in the measurement of the impact of research on other parts of society is ascribed to altmetrics. The present case study investigates the usefulness of altmetrics for measuring the broader impact of research. Methods: This case study is essentially based on a dataset with papers obtained from F1000. The dataset was augmented with altmetrics (such as Twitter counts) which were provided by PLOS (the Public Library of Science). In total, the case study covers a total of 1,082 papers. Findings: The F1000 dataset contains tags on papers which were assigned intellectually by experts and which can characterise a paper. The most interesting tag for altmetric research is "good for teaching". This tag is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialist area. Particularly on Facebook and Twitter, one could expect papers with this tag to be mentioned more often than those without this tag. With respect to the "good for teaching" tag, the results from regression models were able to confirm these expectations: Papers with this tag show significantly higher Facebook and Twitter counts than papers without this tag. This association could not be seen with Mendeley or Figshare counts (that is with counts from platforms which are chiefly of interest in a scientific context). Conclusions: The results of the current study indicate that Facebook and Twitter, but not Figshare or Mendeley, can provide indications of papers which are of interest to a broader circle of readers (and not only for the peers in a specialist area), and seem therefore be useful for societal impact measurement.
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。
