alt metric s - wur...2016/03/14 · let’s vote altmetrics is meant 1. to filter information 2. to...
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ALT METRIC
S March 2016
Ellen Fest, Hugo Besemer
Let’s vote
Altmetrics is meant
1. to filter information
2. to assess impact
Altmetrics measures the impact of publications that are not covered
by Web of Science or Scopus
1. yes
2. no
Altmetrics is not about citations
1. yes
2. no
How it started
2010
“Altmetrics manifesto”
www.altmetrics.org
What they wanted to address
1. Peer review is slow (and
everything gets published
somewhere eventually)
2. Citation counts (and related
measures like H-index) are
slower)
3. Journal Impact Factor is
incorrectly used to assess the
impact of individual articles
Six years later (1)
Six years later (2)
Six years later (3)
Six years later (4)
But there are limitations
And more limitations
Some publishers provide their own metrics
PLOS
● Article Level Metrics
Elsevier
● researcher dashboard
BMC
Frontiers in
Example: PLoS metrics
For all
technicalities
see Bianca
Kramer
Plum analytics: institutional metrics
dashboard
http://plumanalytics.com/learn/about-metrics/
Personal profiles: Impactstory (paid)
Personal profiles: Kudos (1)
Personal profiles: Kudos (2)
Some publishers provide authors with
metrics (1)
Some publishers provide authors with
metrics (2)
Some publishers provide authors with
metrics (3)
What meaning can be read into these
numbers?
They may seen as a proxy for
● Scientific impact
● “Societal impact”
● Buzz
Haustein, S. (2014). Social media in scholarly communication, 42. Retrieved from
http://www.cirst.uqam.ca/Portals/0/docs/Conference/PPT/StefanieHaustein_PPT.pdf
Intermezzo: citation metrics depends on
subject area
Scientist Zacharias Math has a publication from 2003 with 17 citations
Scientist Molecula Biology has a publication from 2009 with 24 citations
Baselines for Mathematics
Baselines for Molecular Biology
0
100
200
300
400
0 2 4 6 8 10 12
Years after publication
Cu
mu
lati
ve
no
. c
ita
tio
ns Baseline
top 10%
top 1%
How do Altmetrics baselines compare to
traditional baselines - traditional
How do Altmetics baselines compare to
traditional baselines – “Alt”
Tweets vs traditional metrics
“It is concluded that the scientific citation process acts relatively independently of the social dynamics on
Twitter.” [1]
“A moderate negative correlation (ρ=-0.390*) is found between the number of publications and tweets
per day, while retweet and citation rates do not correlate.” [2]
“automated Twitter accounts create a considerable amount of tweets to scientific papers and that they
behave differently than common social bots, which has critical implications for the use of raw tweet
counts in research evaluation and assessment” [3]
“The results showed that approximately 76% of the sampled accounts were
maintained by individuals (rather than organizations), 67% of these accounts were maintained by a
single man, and 34.4% of the individuals were identified as possessing a Ph.D, suggesting that the
population of Twitter users who tweet links to academic articles does not reflect the demographics of the
general public” [8]
Mendeley vs citation metrics and reads
“The overall correlation between Mendeley readership counts and citations for the social sciences was higher than for the humanities.” [4]
“…..Mendeley readership can reflect usage similar to traditional citation impact, if the data is restricted to readers who are also authors” [5]
“….it is reasonable to use Mendeley bookmarking counts as an indication of readership because most (55%) users with a Mendeley library had read or intended to read at least half of their bookmarked publications"
Blogs vs citation metrics
“….articles receiving blog citations close to their publication time receive more journal citation later…..” “….7 out of 12 journals (58%) in 2009 and 13 out of 19 journals (68%) in 2010.” [7]
References (1)
[1] J. C. F. de Winter, “The relationship between tweets, citations, and article views for PLOS ONE articles,” Scientometrics, vol.
102, no. 2, pp. 1773–1779, 2014. http://dx.doi.org/10.1007/s11192-014-1445-x
[2] S. Haustein, T. D. Bowman, K. Holmberg, I. Peters, V. Lariviere, and V. Larivière, “Astrophysicists on Twitter: An in-depth
analysis of tweeting and scientific publication behavior,” Aslib J. Inf. Manag., vol. 66, no. 3, pp. 279–296, 2014.
http://www.emeraldinsight.com/10.1108/AJIM-09-2013-0081\nhttp://www.emeraldinsight.com.globalproxy.cvt.dk/journals.htm?issn=2050-
3806&volume=66&issue=3&articleid=17112729&show=html
[3] S. Haustein, T. D. Bowman, K. Holmberg, A. Tsou, C. R. Sugimoto, and V. Larivière, “Tweets as impact indicators: Examining
the implications of automated ‘bot’ accounts on Twitter,” J. Assoc. Inf. Sci. Technol., p. n/a–n/a, 2015.
http://doi.wiley.com/10.1002/asi.23456
[4] E. Mohammadi and M. Thelwall, “Mendeley readership altmetrics for the social sciences and humanities: Research evaluation
and knowledge flows,” J. Assoc. Inf. Sci. Technol., vol. 65, no. 8, pp. 1627–1638, 2014. http://dx.di.org/10.1002/asi.23071
References (2)
[5] E. Mohammadi, M. Thelwall, S. Haustein, and V. Larivière, “Who reads research articles? An altmetrics analysis of Mendeley
user categories,” J. Assoc. Inf. Sci. Technol., vol. 66, no. 9, pp. 1832–1846, 2015. http://dx.doi.org/10.1002/asi.23286
[6] E. Mohammadi, M. Thelwall, K. Kousha “Can Mendeley Bookmarks Reflect Readership ? A Survey of User Literature review
Changes in scholarly reading habits in the digital era,” 2014.
http://www.scit.wlv.ac.uk/~cm1993/papers/CanMendeleyBookmarksReflectReadershipSurvey_preprint.pdf
[7] H. Shema, J. Bar-Ilan, and M. Thelwall, “Do blog citations correlate with a higher number of future citations? Research blogs
as a potential source for alternative metrics,” J. Assoc. Inf. Sci. Technol., vol. 65, no. 5, pp. 1018–1027, 2014.
http://dx.doi.org/10.1002/asi.23037
[8] A. Tsou, T. Bowman, A. Ghazinejad, and C. Sugimoto, “Who Tweets about Science ?,” Issi2015, no. 1, pp. 95–100, 2015.
http://www.issi2015.org/files/downloads/all-papers/0095.pdf
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