NISO Webinar: New Perspectives on Assessment How Altmetrics Measure Scholarly Impact


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NISO Webinar: New Perspectives on Assessment How Altmetrics Measure Scholarly Impact

  1. 1. NISO Webinar: New Perspectives on Assessment How Altmetrics Measure Scholarly Impact November 13, 2013 Speakers: Euan Adie - Founder, Stefanie Haustein, Ph.D. - Research Analyst at Science-Metrix Mike Taylor - Research Specialist, Elsevier Labs
  2. 2. Beyond traditional impact: what can altmetrics do for you? Euan Adie, NISO webinar, 13th November 2013
  3. 3. Several  different  tools   available  
  4. 4. What are “altmetrics”? o  “alternative metrics” o  new ways of measuring different, non-traditional forms of impact, potentially of non-traditional outputs. o  “alternative to only using citations”, not “alternative to citations”. o  complementary to traditional citation-based analysis.
  5. 5. Every researcher is a communicator Within academia Presentations and seminars Funding and ethics applications Academic books Journal articles and posters Term papers and essays Meetings and conferences Correspondence Within society Speaking at public events Books for general audiences Press Social media Blogs Policy documents We should measure both
  6. 6. New perspectives of impact SOCIETAL IMPACT ACADEMIC IMPACT Journal Impact Factor Citation counts Traditional metrics + Download counts Page views Mentions in news reports Mentions in social media Mentions in blogs Reference manager readers … etc. Alternative metrics “altmetrics”
  7. 7. One available tool. We are used a lot by publishers, now some institutions too. We serve ± 2.5 million requests a day.
  8. 8. Where are the readers? Who are the readers?
  9. 9. What’s  interes3ng  about  this   kind  of  data?  
  10. 10. Altmetrics  tools  are  at  version  0.1  
  11. 11. Altmetrics  tools  don’t  (yet)   provide  good  metrics  for   impact     BUT     They  can  help  you  find   evidence  of  impact,  successes  
  12. 12. Evidence of public outreach? An article on the ecological impacts of the Fukushima nuclear accident. •  > 1,859 twitter accounts shared, combined follower count of 2.5M. •  68% of tweets sent from Japan. Figure 1 •  77% of tweets from members of the public. 2012, Scientific Reports 2, 570
  13. 13. A different example, from the USP
  14. 14. Evidence research has reached patients?
  15. 15. PLOS  ALM  Reports:  An  exploratory  review   Engagement/Influence  beyond  citaEons  
  16. 16. PLOS  ALM  Reports:  An  exploratory  review   Engagement/Influence  beyond  citaEons   q  No  cita3ons  in  3  months  since   publica3on.   q  However  TwiJer  men3ons  70x  the   average  ar3cle  in  our  dataset.   q   4x  the  average  for  PLOS  Medicine   ar3cles  in  2013.      
  17. 17. Engagement/Influence  beyond  citaEons   MEP     Centre  for  Bioethics     MEP     Professor  of  EBM     Journal  editor     Health  journalist     NGO     Health,  PopulaEon  &   NutriEon  @  The  World   Bank      
  18. 18. Monitoring  progress:  WT’s  key  indicators   Outcomes   Key  indicators  of  progress   Discoveries       1.  2.  significant  advances  in  the  genera3on  of  new  knowledge   contribute  to  discoveries  with  tangible  impacts  on  health   ApplicaEons         Engagement     3.  contribute  to  the  development  of  enabling  technologies,  products   and  devices   uptake  of  research  into  policy  and  prac3ce         Research  leaders       Research   environment           Influence   4.  5.  6.  7.  8.  enhanced  level  of  informed  debate  in  biomedicine   significant  engagement  of  key  audiences  &  increased  reach   develop  a  cadre  of  research  leaders   evidence  of  significant  career  progression  among  those  we  support   10.  key  contribu3ons  to  the  crea3on,  development  and  maintenance  of   major  research  resources   contribu3ons  to  the  growth  of  centres  of  excellence   11.  12.  significant  impact  on  science  funding  &  policy  developments   significant  impact  on  global  research  priori3es  and  processes     9. 
  19. 19. Quan3fying  aJen3on   Altmetric score
  20. 20. Note  that  we  can  measure   aJen3on,  but…     Posi3ve?    Nega3ve?   For  scien3fic  reasons?   Or  because  the  3tle  is  funny?   Is  364  good  or  bad  anyway?    
  21. 21. Context is everything
  22. 22. In general, altmetrics numbers… X Don’t represent the quality of research. X Don’t indicate the quality of individual researchers. X Don’t tell the whole story – always look for qualitative data as well
  23. 23. Why  score  at  all?  To  allow  ranking  
  24. 24. What  should  we  be  measuring   beyond  aJen3on?   Ques3on  for  the  academic   community.  
  25. 25. Problems  
  26. 26. Problems     •  30  –  40%  of  recent  biomedical   papers  will  have  Altmetric   aJen3on.  But  <  10%  in  social   sciences.  
  27. 27. Problems     •  30  –  40%  of  recent  biomedical   papers  will  have  Altmetric   aJen3on.  But  <  10%  in  social   sciences.   •  Tools  have  subtle  bias:  data   sources  are  mainly  those  popular   in  US,  Europe  
  28. 28. hJp://  
  29. 29. Thanks for listening! E-mail: Twitter: @altmetric Website: Supported by:
  30. 30. Disciplinary differences and other biases Exploring social media metrics in scholarly context Stefanie Haustein @stefhaustein
  31. 31. Overview •  Altmetrics: definitions •  Bibliometrics: in retrospect •  Altmetrics: present •  correlations •  publication age biases •  disciplinary biases •  subject biases •  Altmetrics: future •  References
  32. 32. Altmetrics: definitions •  term coined by Jason Priem •  introduced as a better filter than and alternative to citations and peer-review •  “…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
  33. 33. Altmetrics: definitions •  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)
  34. 34. 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?
  35. 35. Altmetrics: definitions ad-hoc classifications need to be supported by research
  36. 36. Altmetrics: definitions scientist on Twitter tweeting scientific paper in non-scholarly manner: •  scientific impact? •  social impact? •  buzz?
  37. 37. 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 Ø  differentiation between audiences and engagement needed to determine meaning of metrics
  38. 38. 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
  39. 39. 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
  40. 40. 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. Ø  to find out what various social media metrics mean and what they can be used for
  41. 41. 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) 1,136 bibliometrics papers: ρ=0.448 Bar-Ilan et al. (2011) 1,389 F1000 papers: ρ=0.686 Li, & Thelwall (2012) 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=0.35 586,600 PubMed papers: ρ=0.386 Haustein, et al.(submitted)
  42. 42. Altmetrics: age biases Current biases influencing correlation coefficients
  43. 43. Altmetrics: age biases Current biases influencing correlation coefficients
  44. 44. Altmetrics: age biases Current biases influencing correlation coefficients
  45. 45. Altmetrics: age biases Current biases influencing correlation coefficients Ø  compare documents of similar age Ø  normalize for age differences
  46. 46. Altmetrics: disciplinary biases PubMed papers covered by Web of Science 2010-2012
  47. 47. Altmetrics: disciplinary biases PubMed papers covered by Web of Science 2010-2012
  48. 48. 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
  49. 49. 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.
  50. 50. 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
  51. 51. Altmetrics: future •  before applying social media counts in information retrieval and research evaluation, we need: Ø  to understand and define meaning of various social media metrics Ø  to identify different biases Ø  to differentiate between audiences and level of engagement Ø  more transparency and reliability in data aggregation
  52. 52. 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?
  53. 53. Thank you for your attention! Questions? Stefanie Haustein @stefhaustein
  54. 54. Scholarly  connecEons   Cita3ons,  social  media,  ORCID     and  authorship  networks       Mike  Taylor     Research  Specialist   hJp://­‐0002-­‐8534-­‐5985  @herrison    
  55. 55. The  number  of  possible  connec3ons  between   researchers  and  ar3cles,  researchers  and   researchers,  and  ar3cles  and  ar3cles  is   accelera3ng  drama3cally.  Although   bibliometrics  has  been  studied  for  50  years,  the   study  of  these  new  connec3ons  has  only  been   undertaken  recently.  As  infrastructure  is  built  to   accommodate  this  massively  connected  world,   so  research  becomes  enabled  and  desirable.   Part  1  –  formal  links   Part  2  –  informal,  ad  hoc  links  
  56. 56. A  person  writes  an  ar3cle  
  57. 57. A  person  reads  &  cites  other  ar3cles  
  58. 58. Ad  nauseum  
  59. 59. A  person  writes  an  ar3cle  with  another  person  
  60. 60. Not  everyone  did  enough  to  be  an  ‘author’  
  61. 61. Some3mes  people  in  the  same  field   have  the  same  name  
  62. 62. Some3mes  people  with  the  same  name  write   the  same  paper   ?
  63. 63. Some3mes  people  with  the  same  name  get   credit  for  papers  they  didn’t  write   !  
  64. 64. End  of  part  1   Known  facts:   A  person  writes  an  ar3cle   A  person  reads  &  cites  other  ar3cles   A  person  writes  an  ar3cle  with  another  person   -­‐  Bibliometrics     QuesEons  about  facts:   Not  everyone  did  enough  to  be  an  ‘author’   -­‐  Ethics,  acknowledgement  statements,  contributorship     Problems  about  facts:   Some3mes  people  in  the  same  field  have  the  same  name   Some3mes  people  with  the  same  name  write  the  same  paper   Some3mes  people  with  the  same  name  get  credit  for  papers  they  didn’t  write   -­‐  ORCID,  over  300,000  ORCIDs  aler  a  year,  eg,  Elsevier's  editorial  system  has   over  100,000  ar3cles  in  produc3on  with  ORCIDs  
  65. 65. A  person  cites  an  ar3cle  
  66. 66. A  person  does  X  with  an  ar3cle   pins   tweets   Writes  a  blog   Saves  on  delicious   Re-­‐uses   Facebooks   Saves  on   Mendeley   Writes  a  newspaper   ar3cle  
  67. 67. Different  kinds  of  outputs   Saves  on  delicious   pins   Facebooks   tweets   Re-­‐uses   Saves  on   Zotero  /  Mendeley  /   Citeulike  /  biblio  tool   Writes  a  newspaper   ar3cle   Writes  a  blog  
  68. 68. Social  network  ac3vity   Saves  on  delicious   pins   Facebooks   tweets  
  69. 69. Re-­‐using  data,  graphics,  code   Re-­‐uses  
  70. 70. Scholarly  sharing  /  bookmarking  /   recommenda3ons   Saves  on   Zotero  /  Mendeley  /   Citeulike  /  biblio  tool  
  71. 71. Document  crea3on   Writes  a  newspaper   ar3cle   Writes  a  blog  
  72. 72. End  of  part  two   Known  facts:   There  are  more  connec3ons  now  than  have  ever  been   Of  more  types  than  ever   Crea3on  is  ad  hoc,  post  hoc,  technocra3c,  automa3c,  pragma3c,  real-­‐3me…   We  can  count  things  we  don’t  understand     Emergent  thoughts:   An  ORCID  can  be  seen  as  a  document  about  a  person   Links  between  documents  can  be  formed  with  no  human  cura3on  (seman3c   web)     Altmetrics  gives  us  a  view  into  the  world  of  connecEons,  as  a  very  limited   starEng  point:   We  need  research  into  meaning  and  correla3on  before  we  can  make   conclusions  –  researchers   Issues  of  iden3ty,  privacy,  seman3cs,  authorship  /  contributorship,  cura3on   are  all  in3mately  bound  with  altmetrics  
  73. 73. Appendix:  seven  use  cases  for  altmetrics   1.  Predic3on  of  ul3mate  cita3on   2.  Measuring  /  recognizing  component  re-­‐use  /   preparatory  work  /  reproducibility   3.  Hidden  impact  (impact  without  cita3on)   4.  Real-­‐3me  filtering  /  real-­‐3me  evalua3on  (sigint)   5.  Plaporm  /  publisher  /  ins3tu3on  comparison   6.  Measuring  social  reach  /  es3ma3ng  social  impact   7.  Altmetrics  is  of  interest  by  itself  
  74. 74. ! ! ! NISO Webinar: New Perspectives on Assessment How Altmetrics Measure Scholarly Impact ! ! Questions?! All questions will be posted with presenter answers on the NISO website following the webinar:! ! NISO Webinar • November 13, 2013
  75. 75. THANK YOU Thank you for joining us today. 
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