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Altmetrics: A Primer on Data Sources and Integrity

Explore the origins and integrity of altmetric data, understand its various sources and providers, and learn about gaming concerns and strategies to ensure credibility. Discover how to use altmetrics effectively in research evaluation.

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Altmetrics: A Primer on Data Sources and Integrity

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  1. Altmetrics: a primerWhere does the data come from?Can it be gamed?Buy in or build your own? Mike Taylor Research Specialist http://orcid.org/0000-0002-8534-5985 mi.taylor@elsevier.com

  2. What is the data? • A set of altmetric data is about a common document and represents usage, recommendation, shares, re-usage • Identified by DOI, URL, shortened URL, other ID (egArxiv, Pubmed) • It does not show common intent: a tweet is not the same as a Mendeley share is not the same as a Data Dryad data download is not the same as mass media coverage or a blog

  3. Various providers… • Altmetric.com • Impactstory.org • Plum Analytics • PLOS / PLOS code • Altmetrics is not Altmetric.com Each has strengths and weaknesses, no canonical source

  4. Different data have different characteristics • Example from 13,500 papers: • Highly tweeted stories focus on policy, gender, funding, ‘contentious science’ issues, mostly summaries on Nature News • Highly shared papers in Mendeley are hard core original research • Different platforms have discipline bias • Scholarly blogs both lead interest and respond • Data from Altmetric.com

  5. Bringing together sources… • Altmetrics isn’t one thing, so attempting to express it as one thing will fail. • We favour intelligent clusters of data: social activity, mass media, scholarly activity, scholarly comment, re-use • Elsevier believes that more research is needed, and that best indicators are scholarly activity and scholarly comment

  6. Gaming / cheating • If people take this data seriously, will they cheat? • Eg, Brazilian citation scandal, strategies used by people to increase IF of journals • Expertise in detecting fraudulent downloads (eg, SSRN), self-tweeting – when is ‘normal’ corrupt? • One thing to buy 1000 tweets, another to buy 10 blogs, or mass media coverage • Do those twitter accounts have scholarly followers? • Pattern analysis, usage analysis, network analysis • Public data = public analysis = public response

  7. Other criticisms • Biggest criticisms are when people try and conflate all the data into a single thing • Easy point of attack – tweets are all about “sex drugs and rock ‘n’ roll papers”* • Using clusters is more intelligible to academic community – eg, re-use, scholarly activity, scholarly comment (blogs, reviews, discussions) • * this isn’t true anyway

  8. Buy-in, or bake-your-own • Buy-in: Altmetric.com and PLUM from Ebsco • Free-to-use: Impactstory.org, platforms that use Plos article-level-metrics code • Bake-your-own: Impactstory.org, Plos • Or a root-and-branch build

  9. Topics covered • Data sources • Providers • Different types of data, differences and similarities • Criticisms, weaknesses and strategies • Your next steps

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