Scholarly communicationand evaluation:from bibliometrics to altmetrics

Stefanie Hausteinstefanie.haustein@umontreal.ca@stefhausteincrc.ebsi.umontreal.ca/sloan

Scholarly Communication

• peer-reviewed journals1665: 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.”

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

Altmetrics

• alternative use and visibility of publicationson 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 metricsHaustein, 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

Ha

uste

in, B

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)

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 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

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)

Twitter2% 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 attention

Distinguishing between types of Twitter impact

• engagement = dissimilarity with paper title

• exposure = number of followers

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)

Communities of attention

exposure

en

ga

ge

men

tinfluencers /

brokers

orators /

discussing

disseminators /

mumblers

broadcasters

Communities of attention

mean tweets to papers

tp = 5.3

exposure

en

ga

ge

me

nt

tp = 3.2tp = 1.7

tp = 11.5tp = 4.4

(Haustein, Bowman & Costas, submitted)

Communities of attention

mean tweets per day

tpd = 5.9

exposure

en

ga

ge

me

nt

tpd = 10.1tpd = 1.8

tpd = 9.4tpd = 1.7

(Haustein, Bowman & Costas, submitted)

Communities of attention

mean relative citation rate

mncs = 2.3

exposure

en

ga

ge

me

nt

mncs = 2.4mncs = 2.5

mncs = 2.1mncs = 2.2

(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!

Questions?stefanie.haustein@umontreal.ca@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/67/3

Links to OA preprints: crc.ebsi.umontreal.ca/aslib/