Rodrigo Costas & Stefanie Haustein: Citation theories and their application to altmetrics
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Presentation at #2AMconf Rodrigo Costas, (CWTS-Leiden University, the Netherlands) & Stefanie Haustein (Université de Montréal, Canada) Related paper: http://arxiv.org/abs/1502.05701
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Rodrigo Costas & Stefanie Haustein: Citation theories and their application to altmetrics
- 1. Citation theories and their application to altmetrics Rodrigo Costas Centre for Science and Technology Studies (CWTS-Leiden University), the Netherlands Stefanie Haustein Université de Montréal, Canada @RodrigoCostas1 @stefhaustein
- 2. Types of altmetrics • Research impact on (mostly) social media • Heterogeneity • Reference management: reader counts • Recommending: recommendations • Blogging: blog mentions • Microblogging: microposts • others Need to differentiate! Lack of meaning and theoretical foundations 1
- 3. Altmetrics in the light of citation theory • Normative theory (Kaplan, 1965; Merton, 1973) ‘Ethos of science’ (Merton, 1973) • Communism • Universalism • Social constructivist theory Deviation from normative behavior • Persuasion • Matthew effect • Concept symbols (Small, 1978) symbolic act: association of document with concept 2 • Disinterestedness • Organized skepticism • Perfunctory citations • Negative citations, etc.
- 4. Altmetrics in the light of citation theory • Normative theory (Kaplan, 1965; Merton, 1973) ‘Ethos of science’ (Merton, 1973) • Communism • Universalism • Social constructivist theory Deviation from normative behavior • Persuasion • Matthew effect • Concept symbols (Small, 1978) symbolic act: association of document with concept 3 • Disinterestedness • Organized skepticism • Perfunctory citations • Negative citations, etc. How do these theories apply to altmetrics? • Saved in Mendeley • Mentioned in a tweet • Reviewed on F1000 • Cited in a blog post
- 5. Normative theory • F1000 – Faculty members are “world's leading scientists” – Reviewers “must sign a statement to indicate that the article has been selected […] entirely on its scientific merit and […] not been influenced” • Mendeley – Pre-citation context – Anonymous nature of saving process – Not all papers are read 4
- 6. Normative theory • Blogs – similar to citations – “The post author should have read and understood the entire work cited [and] report accurately and thoughtfully on the research.” (ResearchBlogging) – open uncontrolled nature of blogs • Twitter – brevity – humor and entertainment aspect – diffusion channel – diverse user groups and user motivations 5
- 7. Social constructivist theory • Twitter – Matthew effect (internally): reinforced by Twitter affordances extremely skewed distributions – Matthew effect (externally): popularity of high-impact journals • Blogs – Persuasion: driving force in blogging – Matthew effect: focus on high-impact journals 6
- 8. Social constructivist theory • Mendeley – Matthew effect: popularity of high-impact journals – Anonymous nature of saving to Mendeley • F1000 – Matthew effect (externally): focus on high-impact journals – Subjectivity of reviewers – Recommendations are linked to reviewers 7
- 9. Concept symbols • Blogs – Authors associated cited documents with concept • Twitter, F1000, Mendeley – Concepts condensed in tags #altmetrics
- 10. Conclusions • Normative approach • High applicability for F1000 • Moderate for Mendeley • Low applicability for Twitter or blogs • Social constructivist approach • High applicability for Twitter and blogs • Moderate for F1000 • Concept symbols • High applicability for Twitter, F1000, Mendeley and blogs Theory highlights heterogeneity of acts 9
- 11. Conclusions and outlook • Research evaluation • Higher applicability for Mendeley and F1000 • No applicability for Twitter • Content analysis and mapping • High applicability for F1000, Mendeley, Twitter and blogs but as exploration and description of ‘perception of science’ Theoretical discussions help to: • uncover acts behind metrics • interprete meaning of metrics Need for other theories and frameworks 10
Hinweis der Redaktion
- Introduction – SH, RC structure of the presentation So far, there has been an ample empirical focus on the study of altmetrics. In this presentation we aim for a more conceptual discussion of altmetrics. Particularly we are interested in contrasting altmetrics with the most common citation theories used in the discussion of citation analysis. This is justified by the fact that altmetrics have been many times compared and discussed with citations.
- Altmetrics are booming. Technological developments (technology push), new social media tools and the possibility of tracking the mentions to scientific products have open the possibility of new metrics Additionally, there is a growing interest in showing the different types of impact of research (policy pull): scientific, economic and social. Strong hopes have been placed on altmetrics in order to expand the analytical possibilities of the impact of research. However, there is no validation of these metrics. However, altmetrics are characterized by a strong heterogeneity. They are very diverse, created through different platforms, with different motivations and also by different communities of users. This means that they really need to be considered separately. Thus, is not the same the type of data and indicators that can be obtained through for example reference managers like Mendeley, or scholarly recommendations in F1000Prime, in contrast to mentions of publications in scholarly blogs like in ResearchBlogging, or finally the discussion or mentions to research products in Twitter. All these sources are different in conception, design and objective, therefore the indicators and metrics that can be obtained from them are also different. And many more… This heterogeneity itself limits the definition of ‘altmetrics’ as many of them are not that opposed to citations, while others are completely different. In addition to this strong heterogeneity, an important hindrance for the application and usefulness of these metrics is that they lack a proper meaning and understanding of what actually they are measuring. An important reason for this lack of meaning of most altmetric measures is that they also lack theoretical foundations. For this reason (the lack of theoretical foundations), in this presentation we are interested in focusing on the more conceptual discussion of altmetrics, trying to fill this gap.
- In this quest of theoretical foundations for altmetrics, it makes perfect sense to start by discussing them in the light of the theories that have most often used for the discussion and validation of citations. Given the fact that altmetrics are frequently expected to play a similar role to what citations have played in research evaluation, it is reasonable to analyze in which aspects can citation theories help to inform the value and meaning of altmetric indicators, and in which not. The three main citation theories are the following: The Normative theory. This theory is based on the assumption that science is a normative institution governed by internal rewards and sanctions. Thus, citations would be considered indirect indications of intellectual influence, reflecting norms and values through which scholars are expected to acknowledge the use of the cited works. The best presentation of this set of rules and values that rule science was made by Merton in what he called the ‘Ethos of Science’. Thus, Science would be ruled by four basic norms: communism (which can be related to the idea of science as a common good, and here is where the idea of ‘giving credit where credit is due’ can be attributed). Universalism, which basically postulates that all scientists must evaluate the works of others regardless of non-scientific characteristics such as race, nationality, culture or gender. In citation behavior this would imply that citations are the rewards in the science system indicating fair cognitive and intellectual influence. Disinterestedness norm would support the idea that scientists are disinterested ad not seek to gain personal advantages by flattering others or citing themselves. Finally, organized skepticism has to do with scientists treating new claims with skepticism, including their own contributions. The social constructivist theory points that works are cited for a variety of factors, many of which have nothing to do with intellectual debt as explained in the normative theory. Thus, citations would be elements of persuasion to convince others of the goodness of one’s claims. Thus, this theory suggests that there are different motivations for citing, many of them influenced by cognitive style and personality and not necessarily by universalistic reasons. Thus, here we can mention deviations such as the Mathew effect, the use of citations as persuasion tools, the use of perfunctory or superficial citations, the presence of negative citations, etc. Concept symbols theory. This theory considers that citations are symbolic of the idea expressed in the paper. Thus when authors cite, they are associating particularly ideas or concepts with particular documents. In other words, citations are ‘private symbols’ between the citer and the cited document. When documents are repeatedly cited the document’s significance, thus the meaning is transferred through this iterative activity. In essence, this theory explains why retrieval and filtering systems based on citations make sense, as well as maps of science based on citation relationships. THESE TWO THEORIES HAVE OBVIOUS IMPLICATIONS FOR THE USE AND APPLICATION OF CITATIONS IN RESEARCH EVALUATION, AS THEY WOULD JUSTIFY (OR SUPPORT THE CRITICISM) OF CITATIONS IN THE EVALUATION OF SCIENCE.
- Our intention in this presentation is to open a discussion on how these theories could apply to different altmetrics. Namely, we are going to focus on Mendeley readerships, Twitter, F1000 and blog mentions of scientific products.
- Blogs: persuasion (view of the blogger)
- Mendeley Matthew effect internally and externally similarly to citations
- F1000 tags (e.g. ‘new finding’, ‘controversial’, etc.) and recommendations by the referees Twitter: users condensing content, use of hashtags and other affordances Through the aggregation of tags (many people using the same tag for the same paper), then private concept symbols become standard symbols. This is the basic idea of crowdsourcing.
- Research evaluation Acts behind F1000 and Mendeley: closer to act of citing Higher applicability in research evaluation Acts behind Twitter more distant to the act of citing Need of other theories (social capital, attention economics, impression management) and novel frameworks for Twitter Content analysis and science mapping Concept symbols applies to F1000, Mendeley, Twitter and blogs Possibilities of exploring the ‘perception of science’ The importance of concept symbols: allows for exploratory and mining processes (connections, relations between topics, users, etc.) Analysis of connections between concepts and publication Users and publications