Purpose

The main purpose of this study is to explore and validate the question “whether altmetric mentions can predict citations to scholarly articles”. The paper attempts to explore the nature and degree of correlation between altmetrics (from ResearchGate and three social media platforms) and citations.

Design/methodology/approach

A large size data sample of scholarly articles published from India for the year 2016 is obtained from the Web of Science database and the corresponding altmetric data are obtained from ResearchGate and three social media platforms (Twitter, Facebook and blog through Altmetric.com aggregator). Correlations are computed between early altmetric mentions and later citation counts, for data grouped in different disciplinary groups.

Findings

Results show that the correlation between altmetric mentions and citation counts are positive, but weak. Correlations are relatively higher in the case of data from ResearchGate as compared to the data from the three social media platforms. Further, significant disciplinary differences are observed in the degree of correlations between altmetrics and citations.

Research limitations/implications

The results support the idea that altmetrics do not necessarily reflect the same kind of impact as citations. However, articles that get higher altmetric attention early may actually have a slight citation advantage. Further, altmetrics from academic social networks like ResearchGate are more correlated with citations, as compared to social media platforms.

Originality/value

The paper has novelty in two respects. First, it takes altmetric data for a window of about 1–1.5 years after the article publication and citation counts for a longer citation window of about 3–4 years after the publication of article. Second, it is one of the first studies to analyze data from the ResearchGate platform, a popular academic social network, to understand the type and degree of correlations.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2019-0364

中文翻译：

---

海拔高度提及能否预测以后的引用？测试来自ResearchGate和三个社交媒体平台的数据的有效性

目的

这项研究的主要目的是探讨和验证“高度变化的提及是否可以预测对学术文章的引用”这一问题。本文试图探索高度度量（来自ResearchGate和三个社交媒体平台）与引文之间的相关性和程度。

设计/方法/方法

可从Web of Science数据库获取印度2016年发表的大量学术论文数据样本，并从ResearchGate和三个社交媒体平台（Twitter，Facebook和Blog，通过Altmetric.com聚合器）获取相应的海拔高度数据。对于分组在不同学科组中的数据，在早期测高提及和后期引用计数之间计算相关性。

发现

结果表明，测高提及与引文计数之间的相关性为正，但较弱。与三个社交媒体平台的数据相比，ResearchGate数据的相关性相对更高。此外，在测高和引文之间的相关程度上观察到了明显的学科差异。

研究局限/意义

结果支持这样一个观点，即高度度量不一定反映与引用相同的影响。但是，提早引起高度关注的文章实际上可能具有轻微的引用优势。此外，与社交媒体平台相比，来自学术社交网络（如ResearchGate）的测高指标与引用之间的相关性更高。

创意/价值

本文在两个方面具有新颖性。首先，它在文章发表后大约1–1.5年内使用高度测量数据，而在文章发表后大约3–4年内使用更长的引文窗口进行引用计数。其次，这是对来自流行的学术社交网络ResearchGate平台的数据进行分析以了解相关类型和相关程度的首批研究之一。

同行评审

本文的同行评审历史记录可在以下网址获得：https://publons.com/publon/10.1108/OIR-11-2019-0364