Interpreting “altmetrics”: viewing acts on social media through the lens of citation and social theories

“More than 30 years after Cronin’s seminal paper on “the need for a theory of citing” (Cronin, 1981), the metrics community is once again in need of a new theory, this time one for so-called “altmetrics”. Altmetrics, short for alternative (to citation) metrics — and as such a misnomer — refers to a new group of metrics based (largely) on social media events relating to scholarly communication. As current definitions of altmetrics are shaped and limited by active platforms, technical possibilities, and business models of aggregators such as, ImpactStory, PLOS, and Plum Analytics, and as such constantly changing, this work refrains from defining an umbrella term for these very heterogeneous new metrics. Instead a framework is presented that describes acts leading to (online) events on which the metrics are based. These activities occur in the context of social media, such as discussing on Twitter or saving to Mendeley, as well as downloading and citing. The framework groups various types of acts into three categories — accessing, appraising, and applying — and provides examples of actions that lead to visibility and traceability online. To improve the understanding of the acts, which result in online events from which metrics are collected, select citation and social theories are used to interpret the phenomena being measured. Citation theories are used because the new metrics based on these events are supposed to replace or complement citations as indicators of impact. Social theories, on the other hand, are discussed because there is an inherent social aspect to the measurements.”


Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

“A number of new metrics based on social media platforms—grouped under the term “altmetrics”—have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters’ Web of Science as well as social media counts from, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators.”

URL : Characterizing Social Media Metrics of Scholarly Papers

DOI : 10.1371/journal.pone.0120495

New data, new possibilities: Exploring the insides of

“This paper analyzes, one of the most important altmetric data providers currently used. We have analyzed a set of publications with DOI number indexed in the Web of Science during the period 2011-2013 and collected their data with the Altmetric API. 19% of the original set of papers was retrieved from including some altmetric data. We identified 16 different social media sources from which retrieves data. However five of them cover 95.5% of the total set. Twitter (87.1%) and Mendeley (64.8%) have the highest coverage. We conclude that is a transparent, rich and accurate tool for altmetric data. Nevertheless, there are still potential limitations on its exhaustiveness as well as on the selection of social media sources that need further research.”


Altmetrics as a means of assessing scholarly output

“Career progression for scientists involves an assessment of their contribution to their field and a prediction of their future potential. Traditional measures, such as the impact factor of the journal that a researcher publishes in, may not be an appropriate or accurate means of assessing the overall output of an individual. The development of altmetrics offers the potential for fuller assessments of a researcher’s output based on both their traditional and non-traditional scholarly outputs. New tools should make it easier to include non-traditional outputs such as data, software and contributions to peer review in the evaluation of early- and mid-career researchers.”


Novel Research Impact Indicators

Citation counts and more recently usage statistics provide valuable information about the attention and research impact associated with scholarly publications.

The open access publisher Public Library of Science (PLOS) has pioneered the concept of article-level metrics, where these metrics are collected on a per article and not a per journal basis and are complemented by real-time data from the social web or altmetrics: blog posts, social bookmarks, social media and other.

URL : Novel Research Impact Indicators
Alternative URL :

Tracing scientists’ research trends realtimely

In this research, we propose a method to trace scientists’ research trends realtimely. By monitoring the downloads of scientific articles in the journal of Scientometrics for 744 hours, namely one month, we investigate the download statistics.

Then we aggregate the keywords in these downloaded research papers, and analyze the trends of article downloading and keyword downloading. Furthermore, taking both the download of keywords and publication of articles into consideration, we design a method to detect the emerging research trends.

We find that in scientometrics field, social media, new indices to quantify scientific productivity (g-index), webometrics, semantic, text mining, open access are emerging fields that information scientists are focusing on.”