No one can read everything. We rely on filters to make sense of the scholarly literature, but the narrow, traditional filters are being swamped. However, the growth of new, online scholarly tools allows us to make new filters; these altmetrics reflect the broad, rapid impact of scholarship in this burgeoning ecosystem. We call for more tools and research based on altmetrics.
As the volume of academic literature explodes, scholars rely on filters to select the most relevant and significant sources from the rest. Unfortunately, scholarship’s three main filters for importance are failing:
- Peer-review has served scholarship well, but is beginning to show its age. It is slow, encourages conventionality, and fails to hold reviewers accountable. Moreover, given that most papers are eventually published somewhere, peer-review fails to limit the volume of research.
- Citation counting measures are useful, but not sufficient. Metrics like the h-index are even slower than peer-review: a work’s first citation can take years. Citation measures are narrow; influential work may remain uncited. These metrics are narrow; they neglect impact outside the academy, and also ignore the context and reasons for citation.
- The JIF, which measures journals’ average citations per article, is often incorrectly used to assess the impact of individual articles. It’s troubling that the exact details of the JIF are a trade secret, and that significant gaming is relatively easy.
Tomorrow’s filters: altmetrics
In growing numbers, scholars are moving their everyday work to the web. Online reference managers Zotero and Mendeley each claim to store over 40 million articles (making them substantially larger than PubMed); as many as a third of scholars are on Twitter, and a growing number tend scholarly blogs.
These new forms reflect and transmit scholarly impact: that dog-eared (but uncited) article that used to live on a shelf now lives in Mendeley, CiteULike, or Zotero–where we can see and count it. That hallway conversation about a recent finding has moved to blogs and social networks–now, we can listen in. The local genomics dataset has moved to an online repository–now, we can track it. This diverse group of activities forms a composite trace of impact far richer than any available before. We call the elements of this trace altmetrics.
Altmetrics expand our view of what impact looks like, but also of what’s making the impact. This matters because expressions of scholarship are becoming more diverse. Articles are increasingly joined by:
- The sharing of “raw science” like datasets, code, and experimental designs
- Semantic publishing or “nanopublication,” where the citeable unit is an argument or passage rather than entire article.
- Widespread self-publishing via blogging, microblogging, and comments or annotations on existing work.
Because altmetrics are themselves diverse, they’re great for measuring impact in this diverse scholarly ecosystem. In fact, altmetrics will be essential to sift these new forms, since they’re outside the scope of traditional filters. This diversity can also help in measuring the aggregate impact of the research enterprise itself.
Altmetrics are fast, using public APIs to gather data in days or weeks. They’re open–not just the data, but the scripts and algorithms that collect and interpret it. Altmetrics look beyond counting and emphasize semantic content like usernames, timestamps, and tags. Altmetrics aren’t citations, nor are they webometrics; although these latter approaches are related to altmetrics, they are relatively slow, unstructured, and closed.
How can altmetrics improve existing filters?
With altmetrics, we can crowdsource peer-review. Instead of waiting months for two opinions, an article’s impact might be assessed by thousands of conversations and bookmarks in a week. In the short term, this is likely to supplement traditional peer-review, perhaps augmenting rapid review in journals like PLoS ONE, BMC Research Notes, or BMJ Open. In the future, greater participation and better systems for identifying expert contributors may allow peer review to be performed entirely from altmetrics. 
Unlike the JIF, altmetrics reflect the impact of the article itself, not its venue. Unlike citation metrics, altmetrics will track impact outside the academy, impact of influential but uncited work, and impact from sources that aren’t peer-reviewed. Some have suggested altmetrics would be too easy to game; we argue the opposite. The JIF is appallingly open to manipulation; mature altmetrics systems could be more robust, leveraging the diversity of of altmetrics and statistical power of big data to algorithmically detect and correct for fraudulent activity. This approach already works for online advertisers, social news sites, Wikipedia, and search engines.
The speed of altmetrics presents the opportunity to create real-time recommendation and collaborative filtering systems: instead of subscribing to dozens of tables-of-contents, a researcher could get a feed of this week’s most significant work in her field. This becomes especially powerful when combined with quick “alt-publications” like blogs or preprint servers, shrinking the communication cycle from years to weeks or days. Faster, broader impact metrics could also play a role in funding and promotion decisions.
Road map for altmetrics
Speculation regarding altmetrics (Taraborelli, 2008; Neylon and Wu, 2009; Priem and Hemminger, 2010) is beginning to yield to empirical investigation and working tools. Priem and Costello (2010) and Groth and Gurney (2010) find citation on Twitter and blogs respectively. ReaderMeter computes impact indicators from readership in reference management systems. Datacite promotes metrics for datasets. Future work must continue along these lines.
Researchers must ask if altmetrics really reflect impact, or just empty buzz. Work should correlate between altmetrics and existing measures, predict citations from altmetrics, and compare altmetrics with expert evaluation. Application designers should continue to build systems to display altmetrics, develop methods to detect and repair gaming, and create metrics for use and reuse of data. Ultimately, our tools should use the rich semantic data from altmetrics to ask “how and why?” as well as “how many?”
Altmetrics are in their early stages; many questions are unanswered. But given the crisis facing existing filters and the rapid evolution of scholarly communication, the speed, richness, and breadth of altmetrics make them worth investing in.
Dario Taraborelli, Wikimedia Foundation (@readermeter)
Paul Groth, VU University Amsterdam (@pgroth)
Cameron Neylon, Science and Technology Facilities Council (@cameronneylon)
v 1.0 – October 26, 2010
v 1.01 – September 28, 2011: removed dash in alt-metrics
10 Comments
Great ideas – but with respect to divorcing a metric from the publication venue, I’m skeptical that it’s possible. After all, the Matthew Effect became the long tail in web talk.
Also, it might be useful to contrast Altmetrics with usage metrics which are also being proposed as alternatives to traditional citation-based metrics
Hi Christina, that’s a good point, but author-level metrics (and for what matters any aggregate institution-level measures) are already divorced from individual publication outlets, aren’t they?I discuss what I believe to be the main differences between usage metrics and metrics based on richer usage patterns (such as personal bookmarking/annotation) in my COOP ’08 paper linked above. The bottom line is: usage metrics are the equivalent (in terms of robustness) of 1st generation ranking algorithms based on click-through rates.
Nice ideas. Do you mean something like scintilla.nature.com? You end by imploring researchers to invest in alt-metrics, but have not yet answered your own questions on the validity of the new metrics. “Researchers must ask if alt-metrics really reflect impact, or just empty buzz.” That should be the other way round: first show the effect on impact then try to convince researchers. “Work should correlate between alt-metrics and existing measures, predict citations from alt-metrics, and compare alt-metrics with expert evaluation.” This seems to be the way to go.
Hi Steve. As I understand, Scintilla filters news and blog posts, but it does it with keywords rather than measuring impact from a variety of sources. So while it’s a cool project, I wouldn’t call it an alt-metrics tool.The early data suggest alt-metrics measure “real” impact (scare quotes because even the citation people will tell you this is tricky to define). Moreover, it’s encouraging that alt-metrics, like citations, are built around native scholarly processes (saving, linking, etc); we’re not asking for popularity votes, we’re observing the ways scholars naturally interact with their work. So alt-metrics show a lot of promise, and we’d like to see more work in this area.We’re not arguing that alt-metrics is ready for prime-time yet. When we suggest it’s time to invest in alt-metrics, we mean just that: let’s start building systems and doing research and see if alt-metrics live up to their promise. We think they will.
It would have been more honest of you to mention Thomson Reuters response to the Rockefeller University Press article…
http://community.thomsonreuters.com/t5/Citation-Impact-Center/Thomson-Scientific-Corrects-Inaccuracies-In-Editorial/ba-p/717/message-uid/717
Hi Grove,
Thanks for the link to Thomson’s rejoinder to the Rossner, Van Epps and Hill (2007) article “Show me the data,” which we link to above. It’s always nice to have a full perspective on an issue, and there are certainly good arguments for the value of the JIF.
However, I disagree that it’s dishonest for us not to link to it, any more than it was dishonest for you to not link to Rosner et. al’s reply to the reply, “Irreproducible results: a response to Thomson Scientific.” Our goal with the manifesto isn’t to put the Journal Impact Factor on trial; that’s been done enough.
Our point, rather, is to present a better, fuller alternative to the way we measure impact now. I think the JIF, as used today, has deep flaws…but even if it didn’t, why not look into ways to understand and track impact even better?
It would be kind of helpful to let new readers know what ALT in “alt-metrics” stands for. Is it an acronym for something, or does it just stand for “alternative”? If it only means “alternative” why should we believe the various assertions that are made about alt-metrics? Are we really sure that we want ANY metrics at all, or are these just something to enable bureaucrats to get under our feet?
Good thought, Julian. The “alt” does indeed stand for “alternative,” and that should be more evident. We’ll change it in the next version of the manifesto (along with losing the hyphen, which has already disappeared from most of our other altmetrics stuff online).
I don’t think think the term, though, has much to do with the value (or lack thereof) of our assertions. Regardless of what “alt” means, I think it’s increasingly accepted that evaluators (some of whom are “bureaucrats,” but some of whom are fellow scholars on hiring, tenure, and grant committees) and working researchers alike are pretty overwhelmed by the quantity of knowledge being produced. We need ways to get a handle on what’s out there, and what’s good.
Traditional metrics, while useful, have let us down because they only give us a few ways to measure “good.” But eschewing all measurement of science is throwing the baby out with the bathwater. As scientists, we measure things all the time; shouldn’t we examine our own work as closely? Rather than getting rid of metrics (which, let’s face it, are not going away), we should be adding metrics, so that we get a messier but richer picture of what’s going on in science. “Impact,” like lots of things, is hard to define and measure. But certainly using new communication technologies to build our understanding of research impact is a better plan than just throwing up our hands and going home.
re: “As the volume of academic literature explodes, scholars rely on filters to select the most relevant and significant sources from the rest.” Isn’t another filter the journal in which ‘relevant and significant sources’ are published? Most serious researchers combine perusing journal contents pages with literature searching.
@Dana, absolutely the journal is a filtering mechanism. I’d maintain it’s a broken one, because requires lots of expensive manual curation, hides valuable research in peer review for a year or more, permits only binary yes/no filtering, and only supports one judgement per article (since you can’t publish in multiple journals). These were all unavoidable bugs in a system built on paper. But there’s no need to suffer them if we built a system on the Web.
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What would happen to science if Elsevier went down?…
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