Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.
Tweets and Mendeley readers
Two different types of article level metrics
Stefanie Haustein

stefanie.haustein@umontreal.ca...
Overview
•  Altmetrics
•  increasing use
•  meaning?

•  Aim of the studies
•  Data sets and methods
•  Results
•  documen...
Altmetrics: increasing use
•  social media activity around scholarly articles growing by
5% to 10% per month (Adie & Roe, ...
Altmetrics: meaning?
•  ultimate goals
•  similar to but more timely than citations
Ø  predicting scientific impact
•  di...
Altmetrics: meaning?
•  Altmetrics are “representing very different things”
(Lin & Fenner, 2013)

•  unclear what exactly ...
Altmetrics: meaning?

ad-hoc
classifications
need to be
supported
by research
Altmetrics: meaning?
scientist on
Twitter tweeting
scientific paper
in non-scholarly
manner:
•  scientific impact?
•  soci...
Altmetrics: meaning?
Aim of the studies
•  providing empirical evidence of Mendeley reader counts
and tweets of scholarly documents for a large...
Aim of the studies
•  large-scale analysis of tweets and Mendeley readers of
biomedical papers
•  Twitter and Mendeley cov...
Data sets & methods
•  1.4 million PubMed papers covered by WoS
•  publication years: 2010-2012
•  document types: article...
Data sets & methods: framework
Data sets & methods: age biases
Current biases influencing correlation coefficients
Ø  compare documents of similar age
Ø...
Results: documents
•  Twitter coverage is quite low but increasing
•  correlation between tweets and citations is very low...
Results: documents
Top 10 tweeted documents:

catastrophe & topical / web & social media / curious story
scientific discov...
Results: correlations
PubMed papers covered by Web of Science (PY=2011)

Spearman correlations between citations (C), Mend...
Results: disciplines
PubMed papers covered by Web of Science 2010-2012
Altmetrics: disciplinary biases
x-axis:
coverage of
specialty on
platform
y-axis:
correlation
between social
media counts
...
Results: disciplines
General Biomedical Research papers 2011

Scatterplot of number of citations and number of tweets (A, ...
Results: disciplines
Public Health papers 2011

Scatterplot of number of citations and number of tweets (A, ρ=0.074**) and...
Conclusions & outlook
•  uptake, usage intensity and correlations differ between
disciplines and research fields
Ø  socia...
Conclusions & outlook
•  before applying social media counts in information
retrieval and research evaluation further rese...
Haustein, S., Peters, I., Sugimoto, C.R., Thelwall, M., & Larivière, V. (in press). Tweeting
biomedicine: an analysis of t...
Nächste SlideShare
Wird geladen in …5
×

Tweets and Mendeley readers: Two different types of article level metrics

4.128 Aufrufe

Veröffentlicht am

Presentation at APE2014

Veröffentlicht in: Bildung, Technologie, Business

Tweets and Mendeley readers: Two different types of article level metrics

  1. 1. Tweets and Mendeley readers Two different types of article level metrics Stefanie Haustein stefanie.haustein@umontreal.ca @stefhaustein
  2. 2. Overview •  Altmetrics •  increasing use •  meaning? •  Aim of the studies •  Data sets and methods •  Results •  documents •  correlations •  disciplines •  Conclusions & outlook
  3. 3. Altmetrics: increasing use •  social media activity around scholarly articles growing by 5% to 10% per month (Adie & Roe, 2013) •  Mendeley and Twitter largest altmetrics sources •  Mendeley •  521 million bookmarks •  2.7 million users •  32% increase of users from 09/2012 to 09/2013 •  Twitter •  500 million tweets per day •  230 million active users •  39% increase of users from 09/2012 to 09/2013 Adie, E. & Roe, W. (2013). Altmetric: Enriching Scholarly Content with Article-level Discussion and Metrics. Learned Publishing, 26(1), 11-17. Mendeley statistics based on monthly user counts from 10/2010 to 01/2014 on the Mendeley website accessed through the Internet Archive Twitter statistics: https://business.twitter.com/whos-twitter and http://www.sec.gov/Archives/edgar/data/1418091/000119312513400028/d564001ds1a.htm
  4. 4. Altmetrics: meaning? •  ultimate goals •  similar to but more timely than citations Ø  predicting scientific impact •  different, broader impact than captured by citations Ø  measuring societal impact •  impact of various outputs Ø  “value all research products” Piwowar (2013) Piwowar, H. (2013). Value all research products. Nature, 493(7431), 159.
  5. 5. Altmetrics: meaning? •  Altmetrics are “representing very different things” (Lin & Fenner, 2013) •  unclear what exactly they measure: •  scientific impact •  social impact •  “buzz” Lin, J. & Fenner, M. (2013). Altmetrics in evolution: Defining and redefining the ontology of article-level metrics. Information Standards Quarterly, 25(2), 20-26.
  6. 6. Altmetrics: meaning? ad-hoc classifications need to be supported by research
  7. 7. Altmetrics: meaning? scientist on Twitter tweeting scientific paper in non-scholarly manner: •  scientific impact? •  social impact? •  buzz?
  8. 8. Altmetrics: meaning?
  9. 9. Aim of the studies •  providing empirical evidence of Mendeley reader counts and tweets of scholarly documents for a large data set •  generate knowledge about factors influencing popularity of scholarly documents on Mendeley and Twitter •  analyzing the following research questions: •  •  •  •  What is the relationship between social-media and citation counts? How do social-media metrics differ? Which papers are highly tweeted or highly bookmarked? How do these aspects differ across scientific disciplines? Haustein, S., Peters, I., Sugimoto, C.R., Thelwall, M., & Larivière, V. (2014). Tweeting Biomedicine: An Analysis of Tweets and Citations in the Biomedical Literature. Journal of the Association for Information Sciences and Technology. Haustein, S., Larivière, V., Thelwall, M., Amyot, D., & Peters, I. (submitted). Tweets vs. Mendeley readers: How do these two social media metrics differ? IT-Information Technology.
  10. 10. Aim of the studies •  large-scale analysis of tweets and Mendeley readers of biomedical papers •  Twitter and Mendeley coverage •  Twitter and Mendeley user rates •  correlation with citations •  discovering differences between: •  documents •  disciplines & specialties Ø  providing an empirical framework to compare coverage, correlations and user rates
  11. 11. Data sets & methods •  1.4 million PubMed papers covered by WoS •  publication years: 2010-2012 •  document types: articles & reviews •  matching of WoS and PubMed •  tweet counts collected by Altmetric.com •  collection based on PMID, DOI, URL •  matching WoS via PMID •  Mendeley readership data collected via API •  matching title and author names •  journal-based matching of NSF classification
  12. 12. Data sets & methods: framework
  13. 13. Data sets & methods: age biases Current biases influencing correlation coefficients Ø  compare documents of similar age Ø  normalize for age differences
  14. 14. Results: documents •  Twitter coverage is quite low but increasing •  correlation between tweets and citations is very low Publication year Twitter coverage Papers (T≥1) Spearman's ρ Mean Median Maximum T2010 C2010 2.4% 13,763 .104** 2.1 18.3 1 7 237 3,922 T2011 C2011 10.9% 63,801 .183** 2.8 5.7 1 2 963 2,300 T2012 C2012 20.4% 57,365 .110** 2.3 1.3 1 0 477 234 9.4% 134,929 .114** 2.5 5.1 1 1 963 3,922 T2010-2012 C2010-2012
  15. 15. Results: documents 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
  16. 16. Results: correlations PubMed papers covered by Web of Science (PY=2011) Spearman correlations between citations (C), Mendeley readers (R) and tweets (T) for all papers published in 2011 (A, n=586,600), for papers with respectively at least one citation (B, n=410,722), one Mendeley reader (C, n=390,190) or one tweet (D, n=63,800), one Mendeley reader and one tweet (E, n=45,229) and one citation, one Mendeley reader and one tweet (F, n=36,068). All results are significant at the 0.01 level (two-tailed).
  17. 17. Results: disciplines PubMed papers covered by Web of Science 2010-2012
  18. 18. 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
  19. 19. Results: disciplines 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.
  20. 20. Results: disciplines Public Health papers 2011 Scatterplot of number of citations and number of tweets (A, ρ=0.074**) and Mendeley readers (B, ρ=0.351**) for papers published in Public Health in 2011. The respective three most tweeted (A) and read (B) papers are labeled showing the first author.
  21. 21. Conclusions & outlook •  uptake, usage intensity and correlations differ between disciplines and research fields Ø  social media counts of papers from different fields are not directly comparable •  citations, Mendeley readers and tweets reflect different kind of impact on different social groups •  Mendeley seems to mirror use of a broader but still academic audience, largely students and postdocs •  Twitter seems to reflect the popularity among a general public and represents a mix of societal impact, scientific discussion and buzz Ø  the number of Mendeley readers and tweets are two distinct social media metrics
  22. 22. Conclusions & outlook •  before applying social media counts in information retrieval and research evaluation further research is needed: Ø  identifying different factors influencing popularity of scholarly documents on social media Ø  analyzing uptake and usage intensity in various disciplines Ø  differentiating between audiences and engagements
  23. 23. 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 Association for Information Sciences and Technology. Haustein, S., Larivière, V., Thelwall, M., Amyot, D., & Peters, I. (submitted). Tweets vs. Mendeley readers: How do these two social media metrics differ? IT-Information Technology. Thank you for your attention! Questions? Stefanie Haustein stefanie.haustein@umontreal.ca @stefhaustein

×