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社交媒体指标数据质量挑战的综合讨论:四大主要替代计量数据聚合器的广泛比较。

General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators.

机构信息

Centre for Science & Technology Studies (CWTS), Leiden University, Leiden, the Netherlands.

DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy, Stellenbosch University, Stellenbosch, South Africa.

出版信息

PLoS One. 2018 May 17;13(5):e0197326. doi: 10.1371/journal.pone.0197326. eCollection 2018.

Abstract

The data collection and reporting approaches of four major altmetric data aggregators are studied. The main aim of this study is to understand how differences in social media tracking and data collection methodologies can have effects on the analytical use of altmetric data. For this purpose, discrepancies in the metrics across aggregators have been studied in order to understand how the methodological choices adopted by these aggregators can explain the discrepancies found. Our results show that different forms of accessing the data from diverse social media platforms, together with different approaches of collecting, processing, summarizing, and updating social media metrics cause substantial differences in the data and metrics offered by these aggregators. These results highlight the importance that methodological choices in the tracking, collecting, and reporting of altmetric data can have in the analytical value of the data. Some recommendations for altmetric users and data aggregators are proposed and discussed.

摘要

研究了四个主要的 altmetric 数据聚合器的数据收集和报告方法。本研究的主要目的是了解社交媒体跟踪和数据收集方法的差异如何对 altmetric 数据的分析使用产生影响。为此,研究了聚合器之间的指标差异,以了解这些聚合器采用的方法选择如何解释发现的差异。我们的研究结果表明,从不同的社交媒体平台以不同的方式访问数据,以及收集、处理、总结和更新社交媒体指标的不同方法,导致这些聚合器提供的数据和指标存在实质性差异。这些结果强调了在 altmetric 数据的跟踪、收集和报告中方法选择的重要性,这可能会对数据的分析价值产生影响。为 altmetric 用户和数据聚合器提出并讨论了一些建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d0c/5957428/41f0d9e6631b/pone.0197326.g001.jpg

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