presentation at NISO webinar on altmetrics on November 13, 2013 http://www.niso.org/news/events/2013/webinars/altmetrics

1. Disciplinary differences
and other biases
Exploring social media metrics in scholarly context
Stefanie Haustein
stefanie.haustein@umontreal.ca
@stefhaustein

2. Overview
• Altmetrics: definitions
• Bibliometrics: in retrospect
• Altmetrics: present
• correlations
• publication age biases
• disciplinary biases
• subject biases
• Altmetrics: future
• References

3. Altmetrics: definitions
•
•
term coined by Jason Priem
introduced as a better filter
than and alternative to
citations and peer-review
http://altmetrics.org/manifesto/
•
“…altmetrics is a good idea,
but a bad name”
“…we would like to propose
the term influmetrics”
Rousseau & Ye (2013)
•
•
rather complementary than
alternative to citations
social media metrics

4. Altmetrics: definitions
• ultimate goals
•
similar to but more timely than citations
 predicting scientific impact
•
different, broader impact than captured by citations
 measuring societal impact
•
impact of various outputs
 “value all research products”
Piwowar (2013)

5. Altmetrics: definitions
•
Altmetrics are “representing very different things”
(Lin & Fenner, 2013)
•
unclear what exactly they measure:
• scientific impact
• social impact
• “buzz”
• all of the above?

6. Altmetrics: definitions
ad-hoc
classifications
need to be
supported
by research

7. Altmetrics: definitions
scientist on
Twitter tweeting
scientific paper
in non-scholarly
manner:
• scientific impact?
• social impact?
• buzz?

8. Altmetrics: definitions
• complex to define and classify tools and motivations
•
•
scientific and non-scientific audiences cannot be
determined on the platform used
level of engagement differs not only between
platforms but also within:
saving paper to Mendeley library vs. tweeting about it
saving vs. reading
retweeting link vs. discussing content
 differentiation between audiences and engagement
needed to determine meaning of metrics

9. Bibliometrics: in retrospect
• when Garfield created SCI, sociologists of science
analyzed meaning of publications and citations
(Merton, Zuckerman, Cole & Cole, etc.)
• sociological research
• What is it to publish a paper?
• What are the reasons to cite?
• empirical bibliometric research
• disciplinary differences in publication
•
and citation behavior
delay and obsolescence patterns

10. Bibliometrics: in retrospect
• empirical studies helped sociologists to understand
structure and norms of science
• for bibliometricians, studies provided a theoretical
framework and legitimation to use citation analysis
in research evaluation
• knowledge about disciplinary differences and
obsolescence patterns helped to normalize statistics
and create more appropriate indicators

11. Bibliometrics: in retrospect
• similar to development of SCI in the 1960s, social
media metrics have to be analyzed:
• qualitative studies to analyze who, how and why
•
people use various social media platforms
large-scale quantitative studies to determine
differences and biases in terms of disciplines, topics,
document types, publications years, publication types
and sources, author age and affiliation, etc.
 to find out what various social media metrics mean
and what they can be used for

12. Altmetrics: correlations
e.g., Mendeley
• 793 Nature papers: ρ=0.559
•
•
•
•
•
•
•
Li, Thelwall, & Giustini (2012)
820 Science papers: ρ=0.540
1,651 JASIST papers: ρ=0.458
Bar-Ilan (2012)
5,596 PLoS ONE papers: ρ=0.3
Priem, Piwowar, & Hemminger (2012)
Bar-Ilan et al. (2011)
1,136 bibliometrics papers: ρ=0.448
Li, & Thelwall (2012)
1,389 F1000 papers: ρ=0.686
62,647 social science papers: ρ=0.516 Mohammadi & Thelwall (in press)
14,640 humanities papers: ρ=0.428
random sample of
Zahedi, Costas, & Wouters (2013)
200,000 WoS papers: ρ=0.35
Haustein, et al.(submitted)
586,600 PubMed papers: ρ=0.386

13. Altmetrics: age biases
Current biases influencing correlation coefficients

14. Altmetrics: age biases
Current biases influencing correlation coefficients

15. Altmetrics: age biases
Current biases influencing correlation coefficients

16. Altmetrics: age biases
Current biases influencing correlation coefficients
 compare documents of similar age
 normalize for age differences

17. Altmetrics: disciplinary biases
PubMed papers covered by Web of Science 2010-2012

18. Altmetrics: disciplinary biases
PubMed papers covered by Web of Science 2010-2012

19. Altmetrics: disciplinary biases
x-axis:
coverage of
specialty on
platform
y-axis:
correlation
between social
media counts
and citations
bubble size:
intensity of use
based on mean
social media
count rate

20. Altmetrics: subject bias
General Biomedical Research papers 2011
Scatterplot of number of citations and number of tweets (A, ρ=0.181**) and Mendeley readers (B, ρ=0.677**),
bubble size represents number of Mendeley readers (A) and tweets (B). The respective three most tweeted (A)
and read (B) papers are labeled showing the first author.

21. Altmetrics: subject bias
Top 10 tweeted documents:
catastrophe & topical / web & social media / curious story
scientific discovery / health implication / scholarly community
Article
Journal
C
T
Hess et al. (2011). Gain of chromosome band 7q11 in papillary thyroid carcinomas of young patients
is associated with exposure to low-dose irradiation
PNAS
9
963
Yasunari et al. (2011). Cesium-137 deposition and contamination of Japanese soils due to the
Fukushima nuclear accident
PNAS
30
639
Sparrow et al. (2011). Google Effects on Memory: Cognitive Consequences of Having Information at
Our Fingertips
Science
11
558
Onuma et al. (2011). Rebirth of a Dead Belousov–Zhabotinsky Oscillator
Journal of Physical
Chemistry A
--
549
Silverberg (2012). Whey protein precipitating moderate to severe acne flares in 5 teenaged athletes
Cutis
--
477
Wen et al. (2011). Minimum amount of physical activity for reduced mortality and extended life
expectancy: a prospective cohort study
Lancet
51
419
Kramer (2011). Penile Fracture Seems More Likely During Sex Under Stressful Situations
Journal of Sexual
Medicine
--
392
Newman & Feldman (2011). Copyright and Open Access at the Bedside
New England
Journal of Medicine
3
332
Reaves et al. (2012). Absence of Detectable Arsenate in DNA from Arsenate-Grown GFAJ-1 Cells
Science
5
323
Bravo et al. (2011). Ingestion of Lactobacillus strain regulates emotional behavior and central GABA
receptor expression in a mouse via the vagus nerve
PNAS
31
297

22. Altmetrics: future
• before applying social media counts in information
retrieval and research evaluation, we need:
 to understand and define meaning of various
social media metrics
 to identify different biases
 to differentiate between audiences and
level of engagement
 more transparency and reliability in data aggregation

23. References
Bar-Ilan, J. (2011). Articles tagged by 'bibliometrics' on Mendeley and CiteULike. Paper presented at the Metrics 2011 Symposium on
Informetric and Scientometric Research, New Orleans, Louisiana.
Bar-Ilan, J., Haustein, S., Peters, I., Priem, J., Shema, H., & Terliesner, J. (2012). Beyond citations: Scholars' visibility on the social web.
In Proceedings of the 17th International Conference on Science and Technology Indicators, (pp. 98-109).
Haustein, S., Peters, I., Sugimoto, C.R., Thelwall, M., & Larivière, V. (in press). Tweeting biomedicine: an analysis of tweets and citations
in the biomedical literature. Journal of the American Society for Information Science and Technology.
Haustein, S., Bowman, T.D., Holmberg, K., Larivière, V., & Peters, I., (submitted). Astrophysicists on Twitter: An in-depth analysis of
tweeting and scientific publication behavior. Aslib Proceedings.
jasonpriem (2010, September 28). I like the term #articlelevelmetrics, but it fails to imply *diversity* of measures. Lately, I'm liking
#altmetrics. [Twitter post].
Li, X. & Thelwall, M. (2012). F1000, Mendeley and Traditional Bibliometric Indicators. In Proceedings of the 17th International Conference
on Science and Technology Indicators, (pp. 541-551).
Li, X., Thelwall, M., & Giustini, D. (2012). Validating online reference managers for scholarly impact measurement. Scientometrics, 91(2),
461-471.
Lin, J. & Fenner, M. (2013). Altmetrics in evolution: Defining and redefining the ontology of article-level metrics. Information Standards
Quarterly, 25(2), 20-26.
Mohammadi, E., & Thelwall, M. (in press). Mendeley readership altmetrics for the social sciences and humanities: Research evaluation
and knowledge flows. Journal of the American Society for Information Science and Technology.
Piwowar, H. (2013). Value all research products. Nature, 493(7431), 159.
Priem, J., Piwowar, H., & Hemminger, B.M. (2012). Altmetrics in the wild: Using social media to explore scholarly impact. arXiv.
Priem, J., Taraborelli, D., Groth, P. & Neylon, C. (2010). Alt-Metrics: A Manifesto.
Rousseau, R., & Ye, F.Y. (2013). A multi-metric approach for research evaluation. Chinese Science Bulletin, 58(26), 3288-3290.
Zahedi, Z., Costas, R., & Wouters, P. (2013). What is the impact of the publications read by the different Mendeley users? Could they help
to identify alternative types of impact? Presentation held at the PLoS ALM Workshop 2013 in San Francisco.

24. Thank you for your attention!
Questions?
Stefanie Haustein
stefanie.haustein@umontreal.ca
@stefhaustein