The Poor AltmetricPerformance of Publications Authored by Researchers in Mainland China

Xianwen Wang*, Zhichao Fang, Qingchun Li and Xinhui Guo

• WISE Lab, Faculty of Humanities and Social Sciences, Dalian     University of Technology, Dalian, China

China’s scientific output has risen precipitously overthe past decade; it is now the world’s second-largest producer of scientific papers,behind only the United States. The quality of China’s research is also on therise (Van Noorden, 2016). The online visibility andimpact of China’s research are also important issues worth exploring. In thisstudy, we investigate the altmetric performance of publications in the field ofBiotechnology and Applied Microbiology and published by authors from Chineseaffiliations. We find that papers published by those authors from Chineseaffiliations have much lower visibility on the social web than articles fromother countries, when there is no significant difference for the citations.Fewer of China’s publications get tweeted, and those tweeted publications attractless social attention. A geographical analysis of tweeters shows that scholarlyarticles get most of their social attention from the authors’ home countries, afinding that is also confirmed by correlation and regression analysis. Thissituation, which is unfavorable for researchers from Chinese affiliations, iscaused, in part, by the inaccessibility of mainstream social networkingplatforms in mainland China.

Data and Methods

Our research objects are publications in the field of Biotechnology and Applied Microbiology, as classified by Web of Science. There are two reasons for this choice. One is that Biotechnology and Applied Microbiology is one of the most productive and specific subject areas; the other is that this subject overall performs well in altmetrics.

The publication data are harvested from Web of Science, while the altmetrics data are from http://altmetric.com. Because of the open-access advantage, open-access articles are dominant in gaining social media attention (Wang et al., 2015). To avoid errors caused by different access types, all sample articles were chosen from open-access journals; these journals have higher visibility and accessibility via social media than non-open-access publications, which increases the prospect of public consumption and engagement (Mounce, 2013). The publication data for 6,076 articles in the field of Biotechnology and Applied Microbiology, published in open-access journals in 2015, were retrieved from Web of Science. Using DOIs from the downloaded Web of Science records, we collected the altmetric data for the 6,076 records from http://altmetric.com, using their API. The altmetric data include the Altmetric Attention Score and number of tweeters, which are used to measure social buzz about the articles in the dataset. The Altmetric Attention Score is a weighted count of the amount of attention http://altmetric.com picked up for a research output; detailed data sources and weightings of the Altmetric Attention Score can be found at https://help.altmetric.com/support/solutions/articles/6000060969-how-is-the-altmetric-score-calculated. All these data are processed and parsed into a SQL server database for analysis. The final dataset includes the 6,076 identified papers, their Altmetric Attention Scores, the tweeted shares of the papers, and each paper’s citations (if any).

In this study, we determine authors’ locations based on their institutional affiliation. For example, if the author’s institution is located in mainland China, that author is defined as an “author in mainland China.” Here, we use “author in mainland China” instead of “author from mainland China,” because only the authors from institutions located in China are considered, and those Chinese authors with affiliations from other countries are excluded. Accordingly, there are two ways in which the 6,076 articles of the dataset are classified into two groups. In the first method, articles are divided based on the location of all authors’ affiliations: either all article authors are in mainland China or no article authors are in mainland China. In the second classification method, articles are grouped instead by the location of the first or corresponding author’s affiliation.

From: http://journal.frontiersin.org/article/10.3389/frma.2016.00008/full

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