Bornmann Lutz, Haunschild Robin
Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Hofgartenstr. 8, 80539 Munich, Germany.
Max Planck Institute for Solid State Research, Heisenbergstr. 1, 70569 Stuttgart, Germany.
Scientometrics. 2016;107:1405-1422. doi: 10.1007/s11192-016-1893-6. Epub 2016 Feb 27.
One possible way of measuring the broad impact of research (societal impact) quantitatively is the use of alternative metrics (altmetrics). An important source of altmetrics is Twitter, which is a popular microblogging service. In bibliometrics, it is standard to normalize citations for cross-field comparisons. This study deals with the normalization of Twitter counts (TC). The problem with Twitter data is that many papers receive zero tweets or only one tweet. In order to restrict the impact analysis on only those journals producing a considerable Twitter impact, we defined the Twitter Index (TI) containing journals with at least 80 % of the papers with at least 1 tweet each. For all papers in each TI journal, we calculated normalized Twitter percentiles (TP) which range from 0 (no impact) to 100 (highest impact). Thus, the highest impact accounts for the paper with the most tweets compared to the other papers in the journal. TP are proposed to be used for cross-field comparisons. We studied the field-independency of TP in comparison with TC. The results point out that the TP can validly be used particularly in biomedical and health sciences, life and earth sciences, mathematics and computer science, as well as physical sciences and engineering. In a first application of TP, we calculated percentiles for countries. The results show that Denmark, Finland, and Norway are the countries with the most tweeted papers (measured by TP).
定量衡量研究广泛影响（社会影响）的一种可能方法是使用替代计量指标（替代计量学）。替代计量学的一个重要来源是推特，它是一种流行的微博服务。在文献计量学中，为进行跨领域比较对引文进行标准化是标准做法。本研究涉及推特计数（TC）的标准化。推特数据的问题在于，许多论文收到的推文数为零或仅有一条推文。为了将影响分析仅限制在那些产生相当大推特影响的期刊上，我们定义了推特指数（TI），其中包含至少80%的论文每篇至少有一条推文的期刊。对于每个TI期刊中的所有论文，我们计算了标准化推特百分位数（TP），其范围从0（无影响）到100（最高影响）。因此，与期刊中的其他论文相比，推文最多的论文影响最大。建议使用TP进行跨领域比较。我们研究了TP与TC相比的领域独立性。结果指出，TP尤其可有效地用于生物医学与健康科学、生命与地球科学、数学与计算机科学以及物理科学与工程领域。在TP的首次应用中，我们计算了各国的百分位数。结果表明，丹麦、芬兰和挪威是推文最多的国家（以TP衡量）。
