scholarly communication and evaluation: from bibliometrics to altmetrics stefanie haustein...
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Scholarly communicationand evaluation:from bibliometrics to altmetrics
Stefanie [email protected]@stefhausteincrc.ebsi.umontreal.ca/sloan
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
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
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.”
AltmetricsCriticism 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
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
…
…
…
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)
Definitions and terminology
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics altmetrics
adapted from: Björneborn & Ingwersen (2004, p. 1217)
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)
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)
Definitions and terminology
adapted from: Björneborn & Ingwersen (2004, p. 1217)
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics social media metrics
scholarly metrics
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)
Definitions and terminologyActs leading to (online) events used for metrics
RESEARCH OBJECT
Hau
ste
in,
Bo
wm
an &
Co
stas
(20
15)
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?
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 bookmarks2.7 million users32% increase of users from 9/2012 to
09/2013 (Haustein & Larivière,
2014)
Twitter 500 million tweets per day230 million active users39% increase of users from 9/2012 to
09/2013ca. 10% of researchers in professional
context
Prevalence: coverage
Mendeley93% 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)
Prevalence: density
Mean number of events per paper per document typeWoS papers 2012 with DOI
(Haustein, Costas & Larivière, 2015
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)
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)
Popularity: highly tweeted
Highly tweeted Physics paper
Popularity: highly tweeted
Highly tweeted paper
Popularity: highly tweeted
Highly tweeted paper
Communities of attentionDistinguishing between types of Twitter impact• engagement = dissimilarity with paper title
• exposure = number of followers
Communities of attention• 660,149 original tweets (Altmetric.com up to June 2014)
• 237,222 tweeted documents (WoS 2012 with DOI)
• 125,083 unique users• number of tweets to 2012 papers
• mean tweets per day (all tweets up to April 2015)
• mean relative citation rate of tweeted papers
• mean engagement (dissimilarity between tweet and paper title)
• mean exposure (mean number of followers during tweet)
• mean number of followers (April 2015)
• mean number of following (April 2015)
• tweeted document coupling user network(Haustein, Bowman & Costas, submitted)
Communities of attention
exposure
enga
gem
ent
median dissimilarity with paper title
med
ian
num
ber
of f
ollo
wer
s
influencers / brokers
orators / discussing
disseminators / mumblers
broadcasters
tweet text differs from paper title
tweet text is identical to paper title
few followers many followers
Communities of attention
number of users N = 125,083mean tweets to papers tp = 5.3mean tweets per daytpd = 5.9mean relative citation rate mncs = 2.3mean engagement men = 53.3mean exposure mex = 1,382.6mean number of followers mnfers= 2,027.2mean number of following mnfing= 855.6
exposure
enga
gem
ent
N = 29,770tp = 3.2tpd = 10.1mncs = 2.4men = 74.2mex = 2,876.9mnfers= 4,177.3mnfing = 1,327.1
N = 32,768tp = 1.7tpd = 1.8mncs = 2.5men = 75.8mex = 82.7mnfers = 191.4mnfing = 259.0
N = 32,680tp = 11.5tpd = 9.4mncs = 2.1men = 32.7mex= 2,511.2mnfers = 3,396.8mnfing = 1,497.3
N = 29,865tp = 4.4tpd = 1.7mncs = 2.2men = 30.3mex = 84.6mnfers = 178.0mnfing = 267.4
(Haustein, Bowman & Costas, submitted)
Communities of attention
number of users N = 125,083mean tweets to papers tp = 5.3mean tweets per daytpd = 5.9mean relative citation rate mncs = 2.3mean engagement men = 53.3mean exposure mex = 1,382.6mean number of followers mnfers= 2,027.2mean number of following mnfing= 855.6
exposure
enga
gem
ent
N = 29,770tp = 3.2tpd = 10.1mncs = 2.4men = 74.2mex = 2,876.9mnfers= 4,177.3mnfing = 1,327.1
N = 32,768tp = 1.7tpd = 1.8mncs = 2.5men = 75.8mex = 82.7mnfers = 191.4mnfing = 259.0
N = 32,680tp = 11.5tpd = 9.4mncs = 2.1men = 32.7mex= 2,511.2mnfers = 3,396.8mnfing = 1,497.3
N = 29,865tp = 4.4tpd = 1.7mncs = 2.2men = 30.3mex = 84.6mnfers = 178.0mnfing = 267.4
(Haustein, Bowman & Costas, submitted)
Communities of attention
number of users N = 125,083mean tweets to papers tp = 5.3mean tweets per daytpd = 5.9mean relative citation rate mncs = 2.3mean engagement men = 53.3mean exposure mex = 1,382.6mean number of followers mnfers= 2,027.2mean number of following mnfing= 855.6
exposure
enga
gem
ent
N = 29,770tp = 3.2tpd = 10.1mncs = 2.4men = 74.2mex = 2,876.9mnfers= 4,177.3mnfing = 1,327.1
N = 32,768tp = 1.7tpd = 1.8mncs = 2.5men = 75.8mex = 82.7mnfers = 191.4mnfing = 259.0
N = 32,680tp = 11.5tpd = 9.4mncs = 2.1men = 32.7mex= 2,511.2mnfers = 3,396.8mnfing = 1,497.3
N = 29,865tp = 4.4tpd = 1.7mncs = 2.2men = 30.3mex = 84.6mnfers = 178.0mnfing = 267.4
(Haustein, Bowman & Costas, submitted)
Communities of attention
more than 100 tweeted papers
708 of 125,083 users (0.6%)
9 57
130 512
(Haustein, Bowman & Costas, submitted)
Communities of attention
708 of 125,083 users (0.6%)
more than 100 tweeted papers
(Haustein, Bowman & Costas, submitted)
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
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
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!
Stefanie Haustein
Thank you for your attention!
[email protected]@stefhausteincrc.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/67/3Links to OA preprints: crc.ebsi.umontreal.ca/aslib/