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Introducción a la ‘altmetría’: fuentes, limitaciones y posibilidades
Rodrigo Costas
Center for Science and Technology Studies (CWTS-Leiden University)
Julho / 2016
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Introducción a la ‘altmetría’: fuentes, limitaciones y posibilidades
Rodrigo Costas
Center for Science and Technology Studies (CWTS-Leiden University)
7 de Julho 2016
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Outline
• What are altmetrics?• Concept(s)• Sources
• What do we know about altmetrics?• … a bit of theory• Coverage (time, fields and topics)• Main bibliographic characteristics• Others
• What can we do with them?
• The future?
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3
Altmetrics? Concept(s)
• Altmetrics Manifesto (2010) (http://altmetrics.org/manifesto/)
• Wild west
– Simple definition: ‘any metric around research products, except
citations’
– Working definition: events on social and mainstream media platforms
related to scholarly content or scholars […] and are not the same as […]
citations (Haustein, Bowman, Costas, 2015)
– Frequently ‘defined’ by the data providers (e.g. Policy documents)
– Diversity!
• Main challenge: what do they mean?
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Sources• Mendeley
• Altmetric.com
• PlumAnalytics
• ImpactStory
• PLoS – Article-level metrics
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Plum Analytics
8
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PloS ALM
13
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Data inconsistencies
http://dx.doi.org/10.6084/m9.figshare.1041821
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What do we know? A bit of theory
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Framework of ‘acts’ around Research Objects
18
Otlet (1934): “set of facts or ideas
presented in the form of a text or image”
Bourdieu (1975): entities within
the scholarly community.
Access
Appraise
Apply
http://arxiv.org/abs/1502.05701
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A discussion of the ‘promises’• Diversity of scientific publishing
• Strongest argument• More research is still necessary
• Broader dimensions of scientific performance• Do we agree with and understand these new dimensions?• What does a ‘tweet’ or a ‘Mendeley reader ’ mean from a
performance point of view?
• Speed• Is faster always better? Superficiality?• ‘Sleeping beauties’ or ‘Mendel syndrome’
• Openness• Openness is good• Transparency and consistency are more important
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Coverage of publications per source
20
Adapted from: http://dx.doi.org/10.3145/epi.2014.jul.03
Exception to this: Mendeley (>70%)
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Increasing coverage over time
Source: http://www.cwts.nl/pdf/CWTS-WP-2014-001.pdf
From Altmetric.com
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Coverage by fields
22
Citations
Mendeley
http://dx.doi.org/10.1108/AJIM-12-2014-0173
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• Some relevant patterns:
– Twitter: stronger in Social Sciences and General medicine,
weaker in Natural Sciences and Humanities
23
Coverage by fields
http://dx.doi.org/10.1108/AJIM-12-2014-0173
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• Blogs and news media have a strong focus on
multidisciplinary journals!
24
Coverage by fields
http://dx.doi.org/10.1108/AJIM-12-2014-0173
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Coverage by ‘topics’
25
Biomedical &
health
sciences
Social
sciences &
humanities
Mathematics &
Computer
sciences
Physical
sciences &
Engineering
Life &
Earth
sciences
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Topics with the highest PP(tw1)
26
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Main characteristics
• Moderate correlations for Mendeley and good filtering of highly cited publications
http://dx.doi.org/10.1371/journal.pone.0120495
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Distribution by document types
Editorial Material:
An article that gives the opinions
of a person, group, or
organization. Includes editorials,
interviews, commentary,
discussions between individual,
post-paper discussions, round
table symposia, and clinical
conferences
Letters
News items
News, current
events, and recent
developments.
Opinions, discussions, current
events, recent developments
(‘novelty’)
http://dx.doi.org/10.1371/journal.pone.0120495
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Collaboration
y = 0.4766x + 0.7498R² = 0.9706
y = 0.1323x + 0.0796R² = 0.8987
y = 0.0143x + 0.012R² = 0.9247
0
50,000
100,000
150,000
200,000
0.0
2.0
4.0
6.0
8.0
10.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Num
ber
of p
aper
s
Ave
rage
cit
atio
n or
soc
ial m
edia
rat
e
Number of authors per paper
Papers Citations Twitter Blogs Facebook Google+ News
http://dx.doi.org/10.1371/journal.pone.0120495
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Number of pages
y = 0.7907ln(x) + 1.2369R² = 0.5912
y = -0.0061x + 0.8093R² = 0.3756
0
20,000
40,000
60,000
80,000
100,000
120,000
0.0
1.0
2.0
3.0
4.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Num
ber
of p
aper
s
Ave
rage
cita
tion
or
soci
al m
edia
rat
e
Number of pages
papers Citations Twitter Blogs Facebook Google+ News
http://dx.doi.org/10.1371/journal.pone.0120495
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Number of references
y = 0.0553x + 0.9937R² = 0.9343
y = 0.0064x + 0.5542R² = 0.6644
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
0.0
2.0
4.0
6.0
8.0
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102
105
Num
ber o
f pap
ers
Aver
age
cita
tion
or s
ocia
l med
ia ra
te
Number of references
Papers Citations Twitter Blogs Facebook Google+ News
http://dx.doi.org/10.1371/journal.pone.0120495
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Length of the titles
y = 0.4766x + 0.7498R² = 0.9706
y = -0.0039x + 1.18R² = 0.6085
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
0.0
1.0
2.0
3.0
4.0
12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97 102
107
112
117
122
127
132
137
142
147
152
157
162
167
172
177
182
187
192
197
202
Num
ber o
f pap
ers
Aver
age
cita
tion
or s
ocia
l med
ia ra
te
Title length
Papers Citations Twitter Blogs Facebook Google+ News
http://dx.doi.org/10.1371/journal.pone.0120495
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35
Others: funny things
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36
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- Inconsistencies inter-platform (e.g. changes in
readerships counts in Mendeley, duplicates, meta
data errors, noise, etc.)
- Presence of ‘cyborgs’ and ‘bots’ among social
media users (http://doi.org/10.1002/asi.23456)
- Biases: geographic, language or even technical (e.g.
presence of DOIs, tradition of linking to papers,
etc.) (http://dx.doi.org/10.3145/epi.2014.jul.03)
37
Other data problems
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What can we actually do?
• Mendeley
– Indicators! (same as with citations)
– Filtering highly cited publications
– Readership interests by types of users
• Altmetric.com
– Identifying topics with ‘social media interest’
– Altmetric landscapes
– Identification of ‘communities of attention’
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Filtering highly cited publications
Mendeley
Journal Citation
Score
http://arxiv.org/abs/1507.02093
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Readership activity by Mendeley users
40
http://dx.doi.org/10.6084/m9.figshare.1041819
PhDs
Professors
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Twitter landscapes – E.g. African research
41
Maternal mortality
Sexually transmitted
diseases
Plant and soil
Ecology
Malaria
transmission
TuberculosisAIDS
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Communities of attention
42
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Communities of attention - exposure
43
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Future?• Conceptualization of the new metrics
• Value?
• Standardization of tools and data
• Crucial problems in data quality and indicator construction
• Scalability • APIs
• Potential trivialization due to their limited use (“narcissism”)
• Manipulability
• Normalization & reliability
• Production and use of the new metrics
They must be used and accepted by the scientific community!
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Opportunities
• Better understanding of scientific communication
– How are scientific publications perceived by social media users?
– Text analysis of tweets, hashtags, characterization of users
– Model types of impact (social, public attention, Public Understanding
of Science (PUS), ‘misunderstanding’, etc.)
• Possibilities of indicators
– New forms of dissemination (e.g. videos, blogs, etc.)
• Identify blogs, hastags, Twitters, news media, etc.
relevant for a discipline (e.g. to follow by new students)
• Informing scientists/policy makers of ‘communities of
attention’
• Supporting ‘Crowdfunding’ activities (finding ‘topics’,
audiences, etc.)
46
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Practical example: visualize ‘altmetrics’ for a paper with a DOI
• Find on the web a paper with a DOI
(e.g. http://dx.doi.org/10.1073/pnas.1211286109)
• Go to http://www.altmetric.com/bookmarklet.php and drag & drop the ‘Altmetric it’ icon on the paper
• Alternatively write:
http://www.altmetric.com/details.php?doi=10.1073/pnas.1211286109
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Other references
Report: Users, Narcissism and control.
http://research-acumen.eu/wp-content/uploads/Users-narcissism-and-control.pdf
The metric Tide. http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/The,Metric,Tide/2015_metric_tide.pdf
Review on Altmetrics.
http://recyt.fecyt.es/index.php/EPI/article/view/epi.2015.sep.09/21885