What is your opinion on the new development to measure the scientific impact e.g. by counting the number of tweets, the number of Facebook likes, Mendeley reads that represent a book or an article?
Does this new alternative way of bibliometrics have a future or is this just a transient trend?
What is the relevance of Altmetrics to your institution and to you personally?
that's an interesting question that opens up the wide issue of quantification in academia. when those measures are used without a countermeasure to assess quality i think they do a very bad service to academic institutions. some of these scores are doubtful, as for every score and measure, they can be manipulated. Similarly to Google or Facebook these measures need to be adjusted constantly in order to avoid manipulation. this creates new problems that have yet to be adequately considered. I know some institutions now use google scholar but i would be very surprised to see something like Facebook likes rising to a measure of scientific impact.
I also think that a simple counting of Facebook likes or Tweets should not be solely taken into account in order to measure scientific impact. Instead a consideration of several metrics - where Altmetrics can also be included - should create a broader picture that represents a scientist or an institution.
But there are already several commercial plattforms like altmetric.com that have specialized on measuring impact in Web 2.0 applications and in general it seems to me that Altmetrics might play a bigger role in the future.
I am sorry to be a bit old-fashioned here but I think before altmetrics can be considered a measure at all, we need some kind of measurement theory. It took bibliometrics something like 30 years to arrive at what one could consider a measurement theory of citations.
As for altmetrics: How is "scientific impact" defined? How is the definition operationalised, i.e. a systematic link developed between the content of the definition and empirical phenomena? How are altmetrics indicators related to these empirical phenomena?
What we currently have is euphemistically called a "data-driven" approach. Less euphemistically, it is playing around with numbers and wanting them to mean something.
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