presentation at COAR-SPARC Conference 2015, Porto, Portugal, 16 April 2015 Session 4: Assessing Value Chair: Lars Björnshauge https://www.coar-repositories.org/community/events/annual-meeting-2015/programme/

1. Scholarly communication
and evaluation:
from bibliometrics to altmetrics
Stefanie Haustein
stefanie.haustein@umontreal.ca
@stefhaustein
crc.ebsi.umontreal.ca/sloan

2. Scholarly Communication
• peer-reviewed journals
1665: Journal de Sçavans
Philosophical Transactions
replace personal correspondences
• registration
• certification
• dissemination
• archiving
• “Little Science, Big Science”
Derek J. de Solla Price (1963)
exponential growth

3. Scholarly Communication
• citation analysis as retrieval tool to handle
information overload
“It would not be excessive to demand that the thorough
scholar check all papers that have cited or criticized such
papers, if they could be located quickly. The citation index
makes this check practicable.”
• citation analysis as evaluation method
 oversimplification of scientific work and success
publications = productivity | citations = impact
 adverse effects
Garfield, 1955, p. 108

4. Scholarly Communication
• digital revolution
 electronic publishing
• acceleration, openness and diversification of scholarly
output and impact
 open access and open science
• altmetrics manifesto:
Priem, Taraborelli, Groth and Neylon (2010)
“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.”

5. Altmetrics
Criticism against current form of research evaluation:
• peer-reviewed publications in scholarly journals as the only
form of output that “counts”
• particularly against Journal Impact Factor
• citations as the only form of impact that “counts”
Altmetrics as alternatives:
• including all research “products”
• similar but more timely than citations
 predicting scientific impact
• different, broader impact than citations
 measuring societal impact

6. Altmetrics
• alternative use and visibility of publications
on social media:
more traditional forms of use:
• alternative forms of research output
 pragmatic development based on IT developments
…
…
…

7. Definitions and terminology
• webometrics
“Polymorphous mentioning is likely to become a defining feature of Web-
based scholarly communication.”
“There will soon be a critical mass of web-based digital objects and usage
statistics on which to model scholars’ communication behaviors […] and
with which to track their scholarly influence and impact, broadly conceived
and broadly felt.”
• PLOS article level metrics (ALM)
• altmetrics
“study and use of scholarly impact measures based on
activity in online tools and environments”
“a good idea but a bad name”
Priem (2014, p. 266)
Cronin, Snyder, Rosenbaum, Martinson & Callahan (1998, p.1320)
Cronin (2005, p. 196)
Rousseau & Ye (2013, p. 2)

8. Definitions and terminology
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics altmetrics
adapted from: Björneborn & Ingwersen (2004, p. 1217)

9. Definitions and terminology
adapted from: Björneborn & Ingwersen (2004, p. 1217)
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics social media
metrics
social media metrics
Haustein, Larivière, Thelwall, Amyot
& Peters (2014)

10. Definitions and terminology
adapted from: Björneborn & Ingwersen (2004, p. 1217)
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics social media
metrics
social media metrics
“Although social media
metrics seems a better fit as
an umbrella term because it
addresses the social media
ecosystem from which they
are captured, it fails to
incorporate the sources that
are not obtained from social
media platforms (such as
mainstream newspaper
articles or policy documents)
that are collected (for
instance) by Altmetric.com.“
Haustein, Bowman & Costas (2015, p. 3)

11. Definitions and terminology
adapted from: Björneborn & Ingwersen (2004, p. 1217)
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics social media
metrics
scholarly metrics

12. Definitions and terminology
adapted from: Björneborn & Ingwersen (2004, p. 1217)
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics social media
metrics
scholarly metrics
scholarly metrics
“[T]he heterogeneity and
dynamicity of the scholarly
communication landscape
make a suitable umbrella
term elusive. It may be time
to stop labeling these terms
as parallel and oppositional
(i.e., altmetrics vs
bibliometrics) and instead
think of all of them as
available scholarly metrics—
with varying validity
depending on context and
function.“
Haustein, Sugimoto & Larivière (2015, p. 3)

13. Definitions and terminology
Acts leading to (online) events used for metrics
RESEARCH
OBJECT
Haustein,Bowman&Costas(2015)

14. Social media metrics: research
• Which social media metrics are valid impact indicators?
• What kind of impact do the various metrics reflect?
• What is the relationship between social media activity and
bibliometric variables?
• Which content receive the most attention on the platforms?
• Who is engaging with scholarly material on social media
sites?
• What are the motivations behind this use?

15. Prevalence: social media uptake
• social media activity around scholarly articles grows
5% to 10% per month (Adie & Roe, 2013)
• Mendeley and Twitter largest sources for mentions of
scholarly documents:
Mendeley 521 million bookmarks
2.7 million users
32% increase of users from 9/2012 to 09/2013
(Haustein & Larivière, 2014)
Twitter 500 million tweets per day
230 million active users
39% increase of users from 9/2012 to 09/2013
ca. 10% of researchers in professional context

16. Prevalence: coverage
Mendeley
93% of Science articles 2007 (Li, Thelwall & Giustini, 2012)
94% of Nature articles 2007 (Li, Thelwall & Giustini, 2012)
80% of PLOS journals papers 2003-2010 (Priem, Piwowar & Hemminger, 2012)
66% of PubMed/WoS papers 2010-2012 (Haustein et al., 2014a)
63% of WoS papers with DOIs 2005-2011 (Zahedi, Costas & Wouters, 2014)
47% of Social Science WoS papers 2008 (Mohammadi et al., 2014)
35% of Engineering & Techn. WoS papers 2008 (Mohammadi et al., 2014)
31% of Physics WoS papers 2008 (Mohammadi et al., 2014)
13% of Humanities WoS papers 2008 (Mohammadi & Thelwall, 2014)
Twitter
2% of WoS papers with DOIs 2005-2011 (Zahedi, Costas & Wouters, 2014)
9% of PubMed/WoS 2010-2012 (Haustein et al., 2014b)
13% of WoS papers with DOIs July-December 2011 (Costas, Zahedi & Wouters, 2014)
22% of WoS papers with DOIs 2012 (Haustein, Costas & Larivière, 2015)

17. Prevalence: density
Mean number of events per paper per document type
WoS papers 2012 with DOI
(Haustein, Costas & Larivière, 2015

18. Prevalence: density / intensity
Mean number of events per paper
WoS papers with DOIs 2012
all papers / papers with at least one social media event
0.03 / 1.51 Blogs
0.78 / 3.65 Twitter
0.08 / 1.78 Facebook
0.01 / 1.66 Google+
0.01 / 1.54 Mainstream media
PubMed/WoS papers 2010-2012
6.43 / 9.71 Mendeley (Haustein et al., 2014a)
(Haustein, Costas & Larivière, 2015)

19. Similarity: correlations
Spearman correlations with citations
WoS papers with DOIs 2012
all papers / papers with at least one social media event
0.124 / 0.191 Blogs
0.194 / 0.148 Twitter
0.097 / 0.167 Facebook
0.065 / 0.209 Google+
0.083 / 0.199 Mainstream media
PubMed/WoS papers 2011
0.386 / 0.456 Mendeley (Haustein et al., 2014a)
(Haustein, Costas & Larivière, 2015)

20. Popularity: highly tweeted
Highly tweeted Physics paper

21. Popularity: highly tweeted
Highly tweeted paper

22. Popularity: highly tweeted
Highly tweeted paper

23. Communities of attention
Distinguishing between types of Twitter impact
• engagement = dissimilarity with paper title
• exposure = number of followers

24. Communities of attention
• 660,149 original tweets
• 237,222 tweeted documents
• 125,083 unique users
• number of tweets to 2012 papers
• mean tweets per day
• mean relative citation rate of tweeted papers
• mean engagement
• mean exposure
• mean number of followers
• mean number of following
• tweeted document coupling user network
(Haustein, Bowman & Costas, submitted)

25. Communities of attention
exposure
engagement influencers /
brokers
orators /
discussing
disseminators /
mumblers
broadcasters

26. Communities of attention
mean tweets to papers
tp = 5.3
exposure
engagement
tp = 3.2tp = 1.7
tp = 11.5tp = 4.4
(Haustein, Bowman & Costas, submitted)

27. Communities of attention
mean tweets per day
tpd = 5.9
exposure
engagement
tpd = 10.1tpd = 1.8
tpd = 9.4tpd = 1.7
(Haustein, Bowman & Costas, submitted)

28. Communities of attention
mean relative citation rate
mncs = 2.3
exposure
engagement
mncs = 2.4mncs = 2.5
mncs = 2.1mncs = 2.2
(Haustein, Bowman & Costas, submitted)

29. Communities of attention
more than 100 tweeted papers
708 of 125,083 users (0.6%)
9 57
130 512
(Haustein, Bowman & Costas, submitted)

30. Communities of attention
708 of 125,083 users (0.6%)
more than 100 tweeted papers
(Haustein, Bowman & Costas, submitted)

31. Some conclusions
• citations, Mendeley readers and tweets reflect different
kinds of impact on different social groups
• Mendeley seems to mirror use of broader but still
academic audience, largely students and postdocs
• Twitter seems to reflect popularity among general
public and represents mix of societal impact, scientific
discussion and dissemination and buzz
• differences between disciplines, document types and age
 reader counts and tweets cannot be directly compared
without normalization

32. Some conclusions
• fundamental differences between social media metrics
and citations:
• gatekeeping
• community
• engagement
• quantitative and qualitative research needed:
• determine biases and confounding factors
• identify user groups
• identify user motivations and types of use
 meaning of social media metrics needs to be understood
before they are applied to research evaluation

33. Some tips
When using altmetrics:
• time biases apply: don’t use for old papers!
• most metrics only captured for DOIs: remember limitation!
• social media metrics do not replace citations:
don’t substitute!
• social media metrics are heterogeneous: don’t blend!
• document type: don’t compare!
• disciplinary differences: don’t compare!
• not all events reflect use or impact: differentiate!
• motivations and confounding factors unknown: be careful!

34. Stefanie Haustein
Thank you for your attention!
Questions?
stefanie.haustein@umontreal.ca
@stefhaustein
crc.ebsi.umontreal.ca/sloan
Thank you for your attention!
Questions?
Thank you for your attention!
Questions?Obrigada!
Special Issue “Social Media Metrics”
Aslib Journal of Information Management 67(3)
Early View: www.emeraldinsight.com/toc/ajim/67/3
Links to OA preprints: crc.ebsi.umontreal.ca/aslib/