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Research objects, such as datasets and classification standards, are difficult to be incorporated into a document-centric framework of citations, which relies on unique citable works. The Diagnostic and Statistical Manual for Mental Disorder (DSM)—a dominant classification scheme used for mental disorder diagnosis—however provides a unique lens on examining citations to a research object, given that it straddles the boundaries as a single research object with changing manifestations.

Using over 180,000 citations received by the DSM, this paper analyzes how the citation history of DSM is represented by its various versions, and how it is cited in different knowledge domains as an important boundary object.

It shows that all recent DSM versions exhibit a similar citation cascading pattern, which is characterized by a strong replacement effect between two successive versions. Moreover, the shift of the disciplinary contexts of DSM citations can be largely explained by different DSM versions as distinct epistemic objects.

Based on these results, the authors argue that all DSM versions should be treated as a series of connected but distinct citable objects. The work closes with a discussion of the ways in which the existing scholarly infrastructure can be reconfigured to acknowledge and trace a broader array of research objects.

This paper connects quantitative methods and an important sociological concept, i.e. boundary object, to offer deeper insights into the scholarly communication system. Moreover, this work also evaluates how versioning, as a significant yet overlooked attribute of information resources, influenced the citation patterns of citable objects, which will contribute to more material-oriented scientific infrastructures.

The purpose of this paper is to identify criteria for and definitions of disciplinarity, and how they differ between different types of literature.

This synthesis is achieved through a purposive review of three types of literature: explicit conceptualizations of disciplinarity; narrative histories of disciplines; and operationalizations of disciplinarity.

Each angle of discussing disciplinarity presents distinct criteria. However, there are a few common axes upon which conceptualizations, disciplinary narratives, and measurements revolve: communication, social features, topical coherence, and institutions.

There is considerable ambiguity in the concept of a discipline. This is of particular concern in a heightened assessment culture, where decisions about funding and resource allocation are often discipline-dependent (or focussed exclusively on interdisciplinary endeavors). This work explores the varied nature of disciplinarity and, through synthesis of the literature, presents a framework of criteria that can be used to guide science policy makers, scientometricians, administrators, and others interested in defining, constructing, and evaluating disciplines.

The purpose of this paper is to show that there were differences in the use of Twitter by professors at AAU schools. Affordance use differed between the personal and professional tweets of professors as categorized by turkers. Framing behaviors were described that could impact the interpretation of tweets by audience members.

A three phase research design was used that included surveys of professors, categorization of tweets by workers in Amazon’s Mechanical Turk, and categorization of tweets by active professors on Twitter.

There were significant differences found between professors that reported having a Twitter account, significant differences found between types of Twitter accounts (personal, professional, or both), and significant differences in the affordances used in personal and professional tweets. Framing behaviors were described that may assist altmetric researchers in distinguishing between personal and professional tweets.

The study is limited by the sample population, survey instrument, low survey response rate, and low Cohen’s κ.

An overview of various affordances found in Twitter is provided and a novel use of Amazon’s Mechanical Turk for the categorization of tweets is described that can be applied to future altmetric studies.

This work utilizes a socio-technical framework integrating social and psychological theories to interpret results from the tweeting behavior of professors and the interpretation of tweets by workers in Amazon’s Mechanical Turk.

The purpose of this paper is to analyze the disciplinary orientation of scientific publications that were mentioned on different social media platforms, focussing on their differences and similarities with citation counts.

Social media metrics and readership counts, associated with 500,216 publications and their citation data from the Web of Science database, were collected from Altmetric.com and Mendeley. Results are presented through descriptive statistical analyses together with science maps generated with VOSviewer.

The results confirm Mendeley as the most prevalent social media source with similar characteristics to citations in their distribution across fields and their density in average values per publication. The humanities, natural sciences, and engineering disciplines have a much lower presence of social media metrics. Twitter has a stronger focus on general medicine and social sciences. Other sources (blog, Facebook, Google+, and news media mentions) are more prominent in regards to multidisciplinary journals.

This paper reinforces the relevance of Mendeley as a social media source for analytical purposes from a disciplinary perspective, being particularly relevant for the social sciences (together with Twitter). Key implications for the use of social media metrics on the evaluation of research performance (e.g. the concentration of some social media metrics, such as blogs, news items, etc., around multidisciplinary journals) are identified.

The purpose of this case study is to investigate the usefulness of altmetrics for measuring the broader impact of research.

This case study is based on a sample of 1,082 the Public Library of Science (PLOS) journal articles recommended in F1000. The data set includes altmetrics which were provided by PLOS. The F1000 data set contains tags on papers which were assigned by experts to characterise them.

The most relevant tag for altmetric research is “good for teaching”, as it is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialised area. One could expect papers with this tag to be mentioned more often on Facebook and Twitter than those without this tag. The results from regression models were able to confirm these expectations: papers with this tag show significantly higher Facebook and Twitter counts than papers without this tag. This clear association could not be seen with Mendeley or Figshare counts (that is with counts from platforms which are chiefly of interest in a scientific context).

The results of the current study indicate that Facebook and Twitter, but not Figshare or Mendeley, might provide an indication of which papers are of interest to a broader circle of readers (and not only for the peers in a specialist area), and could therefore be useful for the measurement of the societal impact of research.

The purpose of this study is to contribute to the understanding of how the potential of altmetrics varies around the world by measuring the percentage of articles with non-zero metrics (coverage) for articles published from a developing region (Latin America).

This study uses article metadata from a prominent Latin American journal portal, SciELO, and combines it with altmetrics data from Altmetric.com and with data collected by author-written scripts. The study is primarily descriptive, focusing on coverage levels disaggregated by year, country, subject area, and language.

Coverage levels for most of the social media sources studied was zero or negligible. Only three metrics had coverage levels above 2 per cent – Mendeley, Twitter, and Facebook. Of these, Twitter showed the most significant differences with previous studies. Mendeley coverage levels reach those found by previous studies, but it takes up to two years longer for articles to be saved in the reference manager. For the most recent year, coverage was less than half than what was found in previous studies. The coverage levels of Facebook appear similar (around 3 per cent) to that of previous studies.

The Altmetric.com data used for some of the analyses were collected for a six month period. For other analyses, Altmetric.com data were only available for a single country (Brazil).

The results of this study have implications for the altmetrics research community and for any stakeholders interested in using altmetrics for evaluation. It suggests the need of careful sample selection when wishing to make generalizable claims about altmetrics.

The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet about science topics.

The high-dimensional visualisation approach was applied to three science topics to test its effectiveness for longitudinal analysis of message framing on Twitter over two disjoint periods in time. The paper uses coding frames to drive categorisation and visual analytics of tweets discussing the science topics.

The findings point to the potential of this mixed methods approach, as it allows sufficiently high sensitivity to recognise and support the analysis of non-trending as well as trending topics on Twitter.

Three topics are studied, these illustrate a range of frames, but results may not be representative of all science topics.

Funding bodies increasingly encourage scientists to participate in public engagement. As social media provides an avenue actively utilised for public communication, understanding the nature of the dialog on this medium is important for the scientific community and the public at large.

This study differs from standard approaches to the analysis of micropost data, which tend to focus on large-scale data sets. It provides evidence that this approach enables practical and effective analysis of the content of midsize to large collections of microposts.