Altmetrics: a primer Where does the data come from? Can it be gamed? Buy in or build your own? Mike Taylor Research Specialist

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Presentation transcript:

Altmetrics: a primer Where does the data come from? Can it be gamed? Buy in or build your own? Mike Taylor Research Specialist

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 (eg Arxiv, 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 What is the data?

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

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 Different data have different characteristics

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 Bringing together sources…

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 Gaming / cheating

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 Other criticisms

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 Buy-in, or bake-your-own

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