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The	
  evolution of	
  scholarly communication	
  
and	
  the	
  reward system	
  of	
  science
Stefanie	
  Haustein	
   @stefhaustein
Outline
Scholarly	
  communication
From	
  the	
  16th century	
  to	
  Open	
  Science
Bibliometrics
From	
  library	
  management	
  to	
  research	
  evaluation
Altmetrics
Opportunities	
  and	
  challenges
Conclusions	
  and	
  Outlook
Invisible	
  Colleges
http://commons.wikimedia.org/wiki/File:Marin_mersenne.jpg#/media/File:Marin_mersenne.jpg
http://commons.wikimedia.org/wiki/File:Henry_Oldenburg.jpg
Père	
  Marin	
  Mersenne
(1588-­‐1648)
Henry	
  Oldenburg
(1619-­‐1677)
Scientific	
  Societies
http://upload.wikimedia.org/wikipedia/commons/thumb/6/61/Louis_XIV_und_Colbert_in_der_Akademie.jpg/640px-­‐Louis_XIV_und_Colbert_in_der_Akademie.jpg
https://www.rcseng.ac.uk/museums/hunterian/images/lost-­‐museums-­‐2011/the-­‐royal-­‐society-­‐repository/Image%201%20-­‐%20GreshamCollege.jpg/image_preview
http://en.wikipedia.org/wiki/Nullius_in_verba#/media/File:Bookplate_of_the_Royal_Society_(Great_Britain).jpg
L’Académie	
  royale	
  des	
  sciences
22	
  December 1666
The	
  Royal	
  Society
28	
  November 1660
Scientific	
  Journals
http://de.wikipedia.org/wiki/Journal_des_s%C3%A7avans#/media/File:1665_journal_des_scavans_title.jpg
http://en.wikipedia.org/wiki/Philosophical_Transactions_of_the_Royal_Society#/media/File:Philosophical_Transactions_Volume_1_frontispiece.jpg
Le	
  journal	
  des	
  sçavans
5	
  January 1665
Philosophical Transactions
6	
  March	
  1665
Scientific	
  Articles
Harmon,	
  J.E.	
  &	
  Gross,	
  A.G.	
  (2007).	
  The	
  Scientific	
  Literature.	
  A	
  Guided	
  Tour.	
  Chicago:	
  University	
  of	
  Chicago	
  Press.
• Experiments	
  and	
  descriptions	
  of	
  the	
  natural	
  world
• Avoiding	
  “fine	
  speaking”
• Various	
  styles	
  of	
  arguing
• Qualitative	
  and	
  personal	
  judgements
Proportion	
  of	
  IMRaDadoption	
  in	
  medicaljournalsNumberof	
  references1900	
  to	
  2004
Scientific	
  Articles
Larivière,	
  V.,	
  Archambault,	
  É.	
  &	
  Gingras,	
  Y.	
  (2008).	
  Long-­‐term	
  variations	
  in	
  the	
  aging	
  of	
  scientific	
  literature:	
  From	
  exponential	
  growth	
  to	
  steady-­‐state	
  science	
  (1900-­‐2004).	
  Journal	
  of	
  the	
  
American	
  Society	
  for	
  Information	
  Science	
  and	
  Technology,	
  59(2),	
  288-­‐296.
Sollaci,	
  L.B.	
  &	
  Pereira,	
  M.G.	
  (2004).	
  The	
  introduction,	
  methods,	
  results,	
  and	
  discussion	
  (IMRAD)	
  structure:	
  a	
  fifty-­‐year	
  survey.	
  Journal	
  of	
  the	
  Medical	
  Library	
  Association,	
  92(3),	
  364-­‐371
• Professionalized	
  and	
  highly	
  specialized
• Increased	
  focus	
  on	
  data,	
  graphs,	
  tables	
  and	
  theory
• Impersonal,	
  technical	
  and	
  codified
• Style	
  guides	
  and	
  gatekeeping
• Citations
• Introduction,	
  Methods,	
  Results	
  and	
  Discussion
Digital	
  Revolution
arXiv submission	
  statistics	
  from	
  http://arxiv.org/stats/monthly_submissions
Larivière,	
  V.,	
  Lozano,	
  G.A.	
  &	
  Gingras,	
  Y.	
  (2014).	
  Are	
  elite	
  journals	
  declining?	
  Journal	
  of	
  the	
  Association	
  for	
  Information	
  Science	
  and	
  Technology,	
  65(4),	
  649-­‐655.
• Improved access
• Acceleration
• Collaboration
• Peer	
  review
• Distribution	
  of	
  preprints
• Decreasing importance	
  of	
  
scientific journal
• Journal	
  functions
• Diversification	
  of	
  
publication	
  venues
• Symbolic capital	
  of	
  
journals unchanged
Submissionsto	
  arXiv
Share	
  of	
  top	
  1%	
  mostcitedpapers
Academic Publishing Market
Larivière,	
  V.,	
  Haustein,	
  S.,	
  &	
  Mongeon,	
  P.	
  (2015).	
  The	
  oligopoly	
  of	
  academic	
  publishers	
  in	
  the	
  digital	
  era.	
  PLoS ONE,	
  10(6),	
  e0127502.	
  doi:	
  
10.1371/journal.pone.0127502
• Aggravation	
  of	
  
serials	
  crisis
• Elsevier:	
  30%	
  increase	
  
of	
  subscription	
  price	
  
• Profit	
  margins	
  of	
  
commercial	
  publishers	
  
up	
  to	
  40%
• Decline	
  of	
  
scientific	
  societies	
  
as	
  publishers
• >50%	
  of	
  papers	
  
owned	
  by	
  five	
  major	
  
publishers
Open	
  Access
Archambault,	
  É.,	
  Amyot,	
  D.,	
  Deschamps,	
  P.,	
  Nicol,	
  A.,	
  Rebout,	
  L.	
  &	
  Roberge,	
  G.	
  (2013).	
  Proportion	
  of	
  Open	
  Access	
  Peer-­‐Reviewed	
  Papers	
  at	
  the	
  European	
  and	
  
World	
  Levels	
  2004-­‐2011.	
  Report	
  for	
  the	
  European	
  Commission.	
  http://www.science-­‐metrix.com/pdf/SM_EC_OA_Availability_2004-­‐2011.pdf
Budapest	
  Open	
  Access	
  Initiative
“immediate,	
  free	
  availability	
  on	
  the	
  public	
  internet,	
  permitting	
  
any	
  users	
  to	
  read,	
  download,	
  copy,	
  distribute,	
  print,	
  search	
  or	
  
link	
  to	
  the	
  full	
  text	
  of	
  these	
  articles”
• Gold	
  and	
  Green
• Libre	
  and	
  Gratis
• Hybrid
• Elsevier:	
  $500	
  to	
  5,000
• Springer:	
  $3,000
• Wiley:	
  $3,000
Freelyavailablejournal	
  papers2004	
  to	
  2011
Budapest	
  Open	
  Access	
  Initiative	
  (2002)
Open	
  Science
Kraker,	
  P.,	
  Leony,	
  D.,	
  Reinhardt,	
  W.	
  &	
  Beham,	
  G.	
  (2011).	
  The	
  case	
  for	
  an	
  open	
  science	
  in	
  technology	
  enhanced	
  learning.	
  International	
  Journal	
  of	
  Technology	
  
Enhanced	
  Learning,	
  3(6),	
  643-­‐654.
“opening	
  up	
  the	
  research	
  process	
  by	
  making	
  all	
  of	
  its	
  outcomes,	
  
and	
  the	
  way	
  in	
  which	
  these	
  outcomes	
  were	
  achieved,	
  publicly	
  
available	
  on	
  the	
  World	
  Wide	
  Web”
• Open	
  Data
• Open	
  Source
• Open	
  Methodology
• Open	
  Access
• Open	
  Peer	
  Review
Krakeret	
  	
  al.	
  (2011,	
  p.	
  645)
Bibliometrics
Gross,	
  P.L.K.	
  &	
  Gross,	
  E.M.	
  (1927).	
  College	
  libraries	
  and	
  chemical	
  education.	
  Science,	
  66(1713),	
  385-­‐389.
Library	
  collection	
  management	
  
Journalscitedin	
  the	
  Journal	
  of	
  the	
  American	
  Chemical	
  Society1926	
  
Bibliometrics
Garfield,	
  E.	
  (1955).	
  Citation	
  indexes	
  for	
  science.	
  A	
  new	
  dimension	
  in	
  documentation	
  through	
  association	
  of	
  ideas.	
  Science,	
  122,	
  108-­‐111.
Information	
  retrieval
•
“It	
  would	
  not	
  be	
  excessive	
  to	
  demand	
  that	
  the	
  
thorough	
  scholar	
  check	
  all	
  papers	
  that	
  have	
  
cited	
  or	
  criticized	
  such	
  papers,	
  if	
  they	
  could	
  be	
  
located	
  quickly.	
  The	
  citation	
  index	
  makes	
  this	
  
check	
  practicable.”
• Institute	
  for	
  Scientific	
  Information
• Science	
  Citation	
  Index
• Source	
  Author Index
• Citation	
  Index
Garfield	
  (1955,	
  p.	
  108)
Bibliometrics
Price,	
  D.	
  J.	
  d.	
  S.	
  (1961).	
  Science	
  Since	
  Babylon.	
  New	
  Haven	
  /	
  London:	
  Yale	
  University	
  Press,
Price,	
  D.	
  J.	
  d.	
  S.	
  (1963).	
  Little	
  Science,	
  Big	
  Science.	
  New	
  York:	
  Columbia	
  University	
  Press.
Sociology	
  of	
  science
Derek	
  J.	
  de	
  Solla Price
Science	
  since	
  Babylon	
  (1961)
Little	
  Science	
  – Big	
  Science	
  (1963)
Bibliometrics
Merton,	
  R.	
  K.	
  (1988).	
  The	
  Matthew	
  effect	
  in	
  science,	
  II:	
  Cumulative	
  advantage	
  and	
  the	
  symbolism	
  of	
  intellectual	
  property.	
  Isis,	
  79,	
  606–623.	
  
Sociology	
  of	
  science
Robert	
  K.	
  Merton
• Social	
  norms of	
  science
• Communalism
• Universalism
• Disinterestedness
• Organized skepticism
• Matthew	
  effect
“symbolically,	
  [the	
  reference]	
  registers	
  in	
  the	
  enduring	
  archives	
  
the	
  intellectual	
  property	
  of	
  the	
  acknowledged	
  source	
  by	
  
providing	
  a	
  pellet	
  of	
  peer	
  recognition	
  of	
  the	
  knowledge	
  claim”
Merton	
  (1988,	
  p.	
  621)
Bibliometrics
Moed,	
  H.F.,	
  Burger,	
  W.J.M.,	
  Frankfort,	
  J.G,	
  van	
  Raan,	
  A.F.J.	
  (1985).	
  The	
  use	
  of	
  bibliometric	
  data	
  for	
  the	
  measurement	
  of	
  university	
  research	
  performance.	
  
Research	
  Policy,	
  14(3),	
  131-­‐149.	
  
Research	
  evaluation
• Performance	
  measurement and	
  policy instrument
“When	
  used	
  properly,	
  bibliometric	
  indicators	
  can	
  provide	
  a	
  
‘monitoring	
  device’	
  for	
  university	
  research-­‐management	
  and	
  
science	
  policy.	
  They	
  enable	
  research	
  policy-­‐makers	
  to	
  ask	
  relevant	
  
questions	
  of	
  researchers	
  on	
  their	
  scientific	
  performance,	
  in	
  order	
  
to	
  find	
  explanations	
  of	
  the	
  bibliometric	
  results	
  in	
  terms	
  of	
  factors	
  
relevant	
  to	
  policy.”
• Commercialization
Moedet	
  al.	
  (1985,	
  p.	
  131)
Bibliometrics
Research	
  evaluation
• Part	
  of	
  hiring,	
  promotion	
  and	
  funding decisions
• Dashboard	
  tools
Bibliometrics
Hvistendahl,	
  M.	
  (2013).	
  China’s	
  publication	
  bazaar.	
  Science,	
  342(6162),	
  1035-­‐1039.
van	
  Noorden,	
  R.	
  (2013).	
  Brazilian	
  citation	
  scheme	
  outed:	
  Thomson	
  Reuters	
  suspends	
  journals	
  from	
  its	
  rankings	
  for	
  ‘citation	
  stacking’,	
  Nature,	
  500(7464),	
  510-­‐511.	
  
Research	
  evaluation
• Oversimplification
• Publications	
  =	
  productivity
• Citations	
  =	
  impact
• Uninformed	
  use	
  and	
  misuse
• Impact	
  factor
• h-­‐index
• Adverse	
  effects
• “Salami”	
  publishing
• Honorary	
  authorship
• Self-­‐citations
• Citation	
  cartels
Scholarly metrics
Björneborn,	
  L.	
  &	
  Ingwersen,	
  P.	
  (2004),	
  Toward	
  a	
  basic	
  framework	
  for	
  webometrics.	
  Journal	
  of	
  the	
  American	
  Society	
  for	
  Information	
  Science	
  and	
  Technology,	
  
55(14),	
  1216–1227.
Definitions
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics
adaptedfrom:	
  Björneborn&	
  Ingwersen(2004,	
  p.	
  1217)
Scholarly metrics
Otlet,	
  P.	
  (1934).	
  Traité	
  de	
  documentation:	
  le	
  livre	
  sur	
  le	
  livre,	
  théorie	
  et	
  pratique.
Pritchard,	
  P.	
  (1927).	
  Statistical bibliography or	
  bibliometrics?	
  Journal	
  of	
  Documentation,	
  25,	
  348-­‐349..
Bibliometrics
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics
“La	
  «Bibliometrie»	
  sera	
  
la	
  partie	
  définie	
  de	
  la	
  
Bibliologie	
  qui	
  s'occupe	
  
de	
  la	
  mesure	
  ou	
  
quantité	
  appliquée	
  aux	
  
livres.”	
  
“the	
  application	
  of	
  
mathematics	
  and	
  
statistical	
  methods	
  to	
  
books	
  and	
  other	
  media	
  
of	
  communication”
Pritchard	
  (1969,	
  p.	
  348)
Otlet	
  (1934,	
  p.	
  14)
Scholarly metrics
Altmetrics
adaptedfrom:	
  Björneborn&	
  Ingwersen(2004,	
  p.	
  1217)
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics altmetrics
Björneborn,	
  L.	
  &	
  Ingwersen,	
  P.	
  (2004),	
  Toward	
  a	
  basic	
  framework	
  for	
  webometrics.	
  Journal	
  of	
  the	
  American	
  Society	
  for	
  Information	
  Science	
  and	
  Technology,	
  
55(14),	
  1216–1227.
Scholarly metrics
Priem,	
  J.	
  (2014).	
  Altmetrics.	
  In	
  B.	
  Cronin	
  &	
  C.	
  R.	
  Sugimoto (Eds.),	
  Beyond	
  bibliometrics:	
  harnessing multidimensional indicators of	
  performance	
  (pp.	
  263–287).	
  
Cambridge,	
  MA:	
  MIT	
  Press.	
  
Rousseau,	
  R.	
  &	
  Ye,	
  F.	
  (2013).	
  A	
  multi-­‐metric approach for	
  research evaluation.	
  Chinese Science	
  Bulletin,	
  3288–3290.	
  doi:10.1007/s11434-­‐013-­‐5939-­‐3
Altmetrics
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics altmetrics
“study	
  and	
  use	
  of	
  
scholarly	
  impact	
  
measures	
  based	
  on	
  
activity	
  in	
  online	
  tools	
  
and	
  environments”
“a	
  good	
  idea	
  but	
  a	
  
bad	
  name”
Rousseau	
  &	
  Ye(2013,	
  p.	
  3289)
Priem(2014,	
  p.	
  266)
Scholarly metrics
Definition
altmetrics
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics
adaptedfrom:	
  Björneborn&	
  Ingwersen(2004,	
  p.	
  1217)
Scholarly	
  metrics
Björneborn,	
  L.	
  &	
  Ingwersen,	
  P.	
  (2004),	
  Toward	
  a	
  basic	
  framework	
  for	
  webometrics.	
  Journal	
  of	
  the	
  American	
  Society	
  for	
  Information	
  Science	
  and	
  Technology,	
  
55(14),	
  1216–1227.
Scholarly metrics
Haustein,	
  S.,	
  (2016).	
  Grand	
  challenges	
  in	
  altmetrics:	
  heterogeneity,	
  data	
  quality	
  and	
  dependencies.	
  Scientometrics,	
  108(1),	
  413–423.	
  
altmetrics
informetrics
scientometrics
bibliometrics
cybermetrics
webometrics
Scholarly	
  metrics
Acts:  viewing,  reading,  
saving,  diffusing,  
mentioning,  citing,  
reusing,  modifying,  etc.
Scholarly  documents:  
papers,  books,  blog  posts,  
datasets,  code,  etc.
Scholarly  agents:  
researchers,  universities,  
funders,  journals,  etc.
“[S]cholarly metrics	
  are	
  
thus	
  defined	
  as	
  indicators	
  
based	
  on	
  recorded	
  events	
  
of	
  acts	
  […]	
  related	
  to	
  
scholarly	
  documents	
  […]	
  
or	
  scholarly	
  agents	
  […].”
Haustein	
  (2016,	
  p.	
  348)
Altmetrics
Priem,	
  J.,	
  Taraborelli,	
  D.,	
  Groth,	
  P.,	
  &	
  Neylon,	
  C.	
  (2010).	
  Alt-­‐metrics:	
  a	
  manifesto.	
  October.	
  Retrieved from http://altmetrics.org/manifesto/	
  
• Information	
  overload
“We	
  rely	
  on	
  filters	
  to	
  make	
  sense	
  of	
  the	
  scholarly	
  literature,	
  but	
  
the	
  narrow,	
  traditional	
  filters	
  are	
  being	
  swamped.	
  However,	
  the	
  
growth	
  of	
  new,	
  online	
  scholarly	
  tools	
  allows	
  us	
  to	
  make	
  new	
  
filters;	
  these	
  altmetrics	
  reflect	
  the	
  broad,	
  rapid	
  impact	
  of	
  
scholarship	
  in	
  this	
  burgeoning	
  ecosystem.”
• Criticism	
  against	
  current	
  form	
  of	
  research	
  
evaluation
• Alternative	
  forms	
  of	
  research	
  output
• Alternative	
  use	
  and	
  visibility	
  of	
  publications
Priem et  al.  (2010)
Altmetrics
Coverage per	
  platform
Haustein,	
  S.,	
  Costas,	
  R.,	
  &	
  Larivière,	
  V.	
  (2015).	
  Characterizing	
  social	
  media	
  metrics	
  of	
  scholarly	
  papers:	
  The	
  effect	
  of	
  document	
  properties	
  and	
  collaboration	
  
patterns.	
  PLoS ONE,	
  10(5),	
  e0127830.	
  doi:	
  10.1371/journal.pone.0120495
Zahedi,	
  Z.,	
  &	
  Haustein,	
  S.	
  (in	
  preparation).	
  Which	
  document	
  features	
  attract	
  users	
  in	
  Mendeley?	
  An	
  analysis	
  of	
  bibliographic	
  characteristics	
  of	
  Web	
  of	
  Science	
  
publications	
  and	
  Mendeley	
  readership	
  counts.
Mathematics	
  &
Computer	
  Science
Natural	
  Sciences
&	
  Engineering
Life	
  &
Earth Sciences
Biomedical &
Health Sciences
Social	
  Sciences
&	
  Humanities
76,4	
  %
83,7	
  %
91,4	
  %
86,5	
  %
81,7	
  %
Mendeley
7,5	
  %
12,9	
  %
21,6	
  %
31,7	
  %
26,0	
  %
Twitter
Altmetrics
Coverage per	
  discipline
Haustein,	
  S.,	
  Costas,	
  R.,	
  &	
  Larivière,	
  V.	
  (2015).	
  Characterizing	
  social	
  media	
  metrics	
  of	
  scholarly	
  papers:	
  The	
  effect	
  of	
  document	
  properties	
  and	
  collaboration	
  
patterns.	
  PLoS ONE,	
  10(5),	
  e0127830.	
  doi:	
  10.1371/journal.pone.0120495
Zahedi,	
  Z.,	
  &	
  Haustein,	
  S.	
  (in	
  preparation).	
  Which	
  document	
  features	
  attract	
  users	
  in	
  Mendeley?	
  An	
  analysis	
  of	
  bibliographic	
  characteristics	
  of	
  Web	
  of	
  Science	
  
publications	
  and	
  Mendeley	
  readership	
  counts.
Altmetrics
Density and	
  intensity per	
  platform
Intensity
Haustein,	
  S.,	
  Costas,	
  R.,	
  &	
  Larivière,	
  V.	
  (2015).	
  Characterizing	
  social	
  media	
  metrics	
  of	
  scholarly	
  papers:	
  The	
  effect	
  of	
  document	
  properties	
  and	
  collaboration	
  
patterns.	
  PLoS ONE,	
  10(5),	
  e0127830.	
  doi:	
  10.1371/journal.pone.0120495
Zahedi,	
  Z.,	
  &	
  Haustein,	
  S.	
  (in	
  preparation).	
  Which	
  document	
  features	
  attract	
  users	
  in	
  Mendeley?	
  An	
  analysis	
  of	
  bibliographic	
  characteristics	
  of	
  Web	
  of	
  Science	
  
publications	
  and	
  Mendeley	
  readership	
  counts.
Altmetrics
Spearman	
  correlations with citations
Perfectnegativecorrelation
Perfectpositive	
  correlation
Haustein,	
  S.,	
  Larivière,	
  V.,	
  Thelwall,	
  M.,	
  Amyot,	
  D.,	
  &	
  Peters,	
  I.	
  (2014).	
  Tweets	
  vs.	
  Mendeley	
  readers:	
  How	
  do	
  these two social	
  media	
  metrics differ.	
  
Information	
  Technology,	
  56(5),	
  207–215.	
  doi:	
  10.1515/itit-­‐2014-­‐1048
Haustein,	
  S.,	
  Costas,	
  R.,	
  &	
  Larivière,	
  V.	
  (2015).	
  Characterizing	
  social	
  media	
  metrics	
  of	
  scholarly	
  papers:	
  The	
  effect	
  of	
  document	
  properties	
  and	
  collaboration	
  
patterns.	
  PLoS ONE,	
  10(5),	
  e0127830.	
  doi:	
  10.1371/journal.pone.0120495
Altmetrics
Spearman	
  correlations with citations
NSF	
  Subdiscipline General	
  Biomedical Research 2011
Size of data points represents number of Mendeley readers in Twitter graph (left) and number of tweets
in Mendeley graph (right).
Haustein,	
  S.,	
  Larivière,	
  V.,	
  Thelwall,	
  M.,	
  Amyot,	
  D.,	
  &	
  Peters,	
  I.	
  (2014).	
  Tweets	
  vs.	
  Mendeley	
  readers:	
  How	
  do	
  these two social	
  media	
  metrics differ.	
  
Information	
  Technology,	
  56(5),	
  207–215.	
  doi:	
  10.1515/itit-­‐2014-­‐1048
Altmetrics
Document	
  types
Haustein,	
  S.,	
  Costas,	
  R.,	
  &	
  Larivière,	
  V.	
  (2015).	
  Characterizing	
  social	
  media	
  metrics	
  of	
  scholarly	
  papers:	
  The	
  effect	
  of	
  document	
  properties	
  and	
  collaboration	
  
patterns.	
  PLoS ONE,	
  10(5),	
  e0127830.	
  doi:	
  10.1371/journal.pone.0120495
Altmetrics
Highly tweeted
Altmetrics
Highly tweeted
Altmetrics
Highly tweeted
Altmetrics
Highly tweeted
Altmetrics
Altmetrics	
  in	
  the	
  wild
Opportunities and	
  Challenges
• Heterogeneity
• Time	
  and	
  timing
• Audiences	
  and	
  user	
  groups
Altmetrics
Opportunities
• Different acts
• Diverse	
  motivations
Ø Diverse	
  impact
Challenges
• Understanding underlying processes
Ø Determining the	
  meaning of	
  metrics
Heterogeneity of	
  Altmetrics
Heterogeneity
Saving
to	
  Mendeley
Mentioning
in	
  News
Heterogeneity
Recommending
on	
  F1000
Tweeting
Heterogeneity
Bertin,	
  M.,	
  Atanassova,	
  I.,	
  Gingras,	
  Y.,	
  &	
  Larivière,	
  V.	
  (2015).	
  The	
  invariant	
  distribution	
  of	
  references	
  in	
  scientific	
  articles.	
  Journal	
  of	
  the	
  Association	
  for	
  	
  
Information	
  Science	
  and	
  Technology, 67(1),	
  164-­‐177.	
  doi:	
  10.1002/asi.23367
Distribution	
  of	
  referencesalongthe	
  IMRaDstructure
Citing in	
  a
journal	
  article
Heterogeneity
Acts referring to	
  research objects
Haustein,	
  S.,	
  Bowman,	
  T.	
  D.,	
  &	
  Costas,	
  R.	
  (2016).	
  Interpreting “altmetrics”:	
  Viewing acts on	
  social	
  media	
  through the	
  lens of	
  citation	
  and	
  social	
  theories.	
  Dans	
  
C.	
  R.	
  Sugimoto (dir.),	
  Theories of	
  Informetrics and	
  Scholarly Communication	
  (p.	
  372–405).	
  Berlin:	
  De	
  Gruyter Mouton.	
  doi:	
  10.1515/9783110308464-­‐022
RESEARCH  OBJECT
Time	
  and	
  Timing
Opportunities
• Detailed life	
  cycle	
  of	
  scientific output
Ø Fine-­‐grained indicators and	
  adequate benchmarks
Challenges
• Versions	
  of	
  research output
• Publication	
  dates
Time	
  and	
  Timing
Journal	
  article
• Submitted manuscript
• Revised manuscript
• Accepted manuscript
• Version	
  of	
  Record
• Online	
  publication
• Journal	
  issue
• Online	
  date
• Issue	
  month
Ø Adjusting indicators
3	
  March	
  2014
15	
  July	
  2014
21	
  January2015
February2015
Time	
  and	
  Timing
Time	
  and	
  Timing
Tweets	
  before publication?
Time	
  and	
  Timing
Weekday effects on	
  Twitter
weekday of
online publication:
based on:
8,765 Springer papers with
online publication date
19,010 tweets received within one year
of online publication date
Audiences	
  and	
  User	
  Groups
Opportunities
• Differentiating between types	
  of	
  users
• Measuring societal impact
Challenges
• Identifying users and	
  user	
  groups
• Determining engagement
Audiences	
  and	
  User	
  Groups
Alperin,	
  J.	
  P.	
  (2015).	
  Moving	
  beyond	
  counts:	
  A	
  method	
  for	
  surveying	
  Twitter	
  users.	
  In	
  altmetrics15:	
  5	
  years	
  in,	
  what	
  do	
  we	
  know?	
  Amsterdam,	
  
The	
  Netherlands.	
  Retrieved	
  from:	
  http://altmetrics.org/wp-­‐content/uploads/2015/09/altmetrics15_paper_3.pdf
Tsou,	
  A.,	
  Bowman,	
  T.	
  D.,	
  Ghazinejad,	
  A.,	
  &	
  Sugimoto,	
  C.	
  R.	
  (2015).	
  Who tweets	
  about	
  science?	
  In	
  Proceedings of	
  the	
  2015	
  International	
  Society	
  for	
  
Scientometrics and	
  Informetrics (pp.	
  95–100).	
  Istanbul,	
  Turkey.
Identifying Twitter	
  users
• Altmetric.com	
  classification
• Among	
  a	
  random	
  sample	
  of	
  2,000	
  accounts	
  tweeting	
  
papers,	
  34%	
  of	
  individuals	
  identified	
  as	
  having	
  PhD
• Of	
  286	
  users	
  linking	
  to	
  SciELO articles,	
  24%	
  employed	
  at	
  
university,	
  23%	
  students,	
  36%	
  not	
  university	
  affiliated
*based on	
  Altmetric.com	
  data	
  06/2015
(Tsou,  Bowman,  Ghazinejad,  &  Sugimoto,  2010)
(Alperin,  2015)
1
2
3
Audiences	
  and	
  User	
  Groups
Haustein,	
  S.,	
  &	
  Costas,	
  R.,	
  (2015).	
  Identifying Twitter	
  audiences:	
  who is tweeting about	
  scientific papers?	
  Communication	
  présentée	
  au	
  SIG/MET	
  Workshop,	
  
ASIS&T	
  2015	
  Annual Meeting,	
  7	
  novembre	
  2015,	
  Saint-­‐Louis,	
  MO	
  (USA).
topics  and
collectives
academic
personal
Node size
number of	
  accounts
associated with term
Node color
cluster	
  affiliation
Terms	
  in	
  Twitter	
  bio
Audiences	
  and	
  User	
  Groups
Engagement	
  with scientific papers on	
  Twitter
Haustein,	
  S.,	
  Bowman,	
  T.	
  D.,	
  Holmberg,	
  K.,	
  Tsou,	
  A.,	
  Sugimoto,	
  C.	
  R.,	
  &	
  Larivière,	
  V.	
  (2016).	
  Tweets	
  as	
  impact	
  indicators:	
  Examining	
  the	
  implications	
  of	
  
automated	
  bot	
  accounts	
  on	
  Twitter.	
  Journal	
  of	
  the	
  Association	
  for	
  Information	
  Science	
  and	
  Technology,	
  67(1),	
  232–238.	
  doi:	
  10.1002/asi.23456
Audiences	
  and	
  User	
  Groups
Twitter	
  arXiv bots
Audiences	
  and	
  User	
  Groups
Journal	
  accounts
Conclusions
• Scholarly communication	
  and	
  the	
  reward system	
  
of	
  science	
  are	
  changing
• Potential to	
  become more	
  transparent	
  and	
  
diverse
• Open	
  Science
• Scholarly metrics
• Fundamental difference between posting on	
  
social	
  media	
  and	
  academic publishing
• More	
  metrics =	
  more	
  complexity
Outlook
More	
  scientometric studies.
More	
  sociological research.
Avoid adverse	
  effects.

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Haustein, S. (2017). The evolution of scholarly communication and the reward system of science

  • 1. The  evolution of  scholarly communication   and  the  reward system  of  science Stefanie  Haustein   @stefhaustein
  • 2. Outline Scholarly  communication From  the  16th century  to  Open  Science Bibliometrics From  library  management  to  research  evaluation Altmetrics Opportunities  and  challenges Conclusions  and  Outlook
  • 3.
  • 7. Scientific  Articles Harmon,  J.E.  &  Gross,  A.G.  (2007).  The  Scientific  Literature.  A  Guided  Tour.  Chicago:  University  of  Chicago  Press. • Experiments  and  descriptions  of  the  natural  world • Avoiding  “fine  speaking” • Various  styles  of  arguing • Qualitative  and  personal  judgements
  • 8. Proportion  of  IMRaDadoption  in  medicaljournalsNumberof  references1900  to  2004 Scientific  Articles Larivière,  V.,  Archambault,  É.  &  Gingras,  Y.  (2008).  Long-­‐term  variations  in  the  aging  of  scientific  literature:  From  exponential  growth  to  steady-­‐state  science  (1900-­‐2004).  Journal  of  the   American  Society  for  Information  Science  and  Technology,  59(2),  288-­‐296. Sollaci,  L.B.  &  Pereira,  M.G.  (2004).  The  introduction,  methods,  results,  and  discussion  (IMRAD)  structure:  a  fifty-­‐year  survey.  Journal  of  the  Medical  Library  Association,  92(3),  364-­‐371 • Professionalized  and  highly  specialized • Increased  focus  on  data,  graphs,  tables  and  theory • Impersonal,  technical  and  codified • Style  guides  and  gatekeeping • Citations • Introduction,  Methods,  Results  and  Discussion
  • 9. Digital  Revolution arXiv submission  statistics  from  http://arxiv.org/stats/monthly_submissions Larivière,  V.,  Lozano,  G.A.  &  Gingras,  Y.  (2014).  Are  elite  journals  declining?  Journal  of  the  Association  for  Information  Science  and  Technology,  65(4),  649-­‐655. • Improved access • Acceleration • Collaboration • Peer  review • Distribution  of  preprints • Decreasing importance  of   scientific journal • Journal  functions • Diversification  of   publication  venues • Symbolic capital  of   journals unchanged Submissionsto  arXiv Share  of  top  1%  mostcitedpapers
  • 10. Academic Publishing Market Larivière,  V.,  Haustein,  S.,  &  Mongeon,  P.  (2015).  The  oligopoly  of  academic  publishers  in  the  digital  era.  PLoS ONE,  10(6),  e0127502.  doi:   10.1371/journal.pone.0127502 • Aggravation  of   serials  crisis • Elsevier:  30%  increase   of  subscription  price   • Profit  margins  of   commercial  publishers   up  to  40% • Decline  of   scientific  societies   as  publishers • >50%  of  papers   owned  by  five  major   publishers
  • 11. Open  Access Archambault,  É.,  Amyot,  D.,  Deschamps,  P.,  Nicol,  A.,  Rebout,  L.  &  Roberge,  G.  (2013).  Proportion  of  Open  Access  Peer-­‐Reviewed  Papers  at  the  European  and   World  Levels  2004-­‐2011.  Report  for  the  European  Commission.  http://www.science-­‐metrix.com/pdf/SM_EC_OA_Availability_2004-­‐2011.pdf Budapest  Open  Access  Initiative “immediate,  free  availability  on  the  public  internet,  permitting   any  users  to  read,  download,  copy,  distribute,  print,  search  or   link  to  the  full  text  of  these  articles” • Gold  and  Green • Libre  and  Gratis • Hybrid • Elsevier:  $500  to  5,000 • Springer:  $3,000 • Wiley:  $3,000 Freelyavailablejournal  papers2004  to  2011 Budapest  Open  Access  Initiative  (2002)
  • 12. Open  Science Kraker,  P.,  Leony,  D.,  Reinhardt,  W.  &  Beham,  G.  (2011).  The  case  for  an  open  science  in  technology  enhanced  learning.  International  Journal  of  Technology   Enhanced  Learning,  3(6),  643-­‐654. “opening  up  the  research  process  by  making  all  of  its  outcomes,   and  the  way  in  which  these  outcomes  were  achieved,  publicly   available  on  the  World  Wide  Web” • Open  Data • Open  Source • Open  Methodology • Open  Access • Open  Peer  Review Krakeret    al.  (2011,  p.  645)
  • 13.
  • 14. Bibliometrics Gross,  P.L.K.  &  Gross,  E.M.  (1927).  College  libraries  and  chemical  education.  Science,  66(1713),  385-­‐389. Library  collection  management   Journalscitedin  the  Journal  of  the  American  Chemical  Society1926  
  • 15. Bibliometrics Garfield,  E.  (1955).  Citation  indexes  for  science.  A  new  dimension  in  documentation  through  association  of  ideas.  Science,  122,  108-­‐111. Information  retrieval • “It  would  not  be  excessive  to  demand  that  the   thorough  scholar  check  all  papers  that  have   cited  or  criticized  such  papers,  if  they  could  be   located  quickly.  The  citation  index  makes  this   check  practicable.” • Institute  for  Scientific  Information • Science  Citation  Index • Source  Author Index • Citation  Index Garfield  (1955,  p.  108)
  • 16. Bibliometrics Price,  D.  J.  d.  S.  (1961).  Science  Since  Babylon.  New  Haven  /  London:  Yale  University  Press, Price,  D.  J.  d.  S.  (1963).  Little  Science,  Big  Science.  New  York:  Columbia  University  Press. Sociology  of  science Derek  J.  de  Solla Price Science  since  Babylon  (1961) Little  Science  – Big  Science  (1963)
  • 17. Bibliometrics Merton,  R.  K.  (1988).  The  Matthew  effect  in  science,  II:  Cumulative  advantage  and  the  symbolism  of  intellectual  property.  Isis,  79,  606–623.   Sociology  of  science Robert  K.  Merton • Social  norms of  science • Communalism • Universalism • Disinterestedness • Organized skepticism • Matthew  effect “symbolically,  [the  reference]  registers  in  the  enduring  archives   the  intellectual  property  of  the  acknowledged  source  by   providing  a  pellet  of  peer  recognition  of  the  knowledge  claim” Merton  (1988,  p.  621)
  • 18. Bibliometrics Moed,  H.F.,  Burger,  W.J.M.,  Frankfort,  J.G,  van  Raan,  A.F.J.  (1985).  The  use  of  bibliometric  data  for  the  measurement  of  university  research  performance.   Research  Policy,  14(3),  131-­‐149.   Research  evaluation • Performance  measurement and  policy instrument “When  used  properly,  bibliometric  indicators  can  provide  a   ‘monitoring  device’  for  university  research-­‐management  and   science  policy.  They  enable  research  policy-­‐makers  to  ask  relevant   questions  of  researchers  on  their  scientific  performance,  in  order   to  find  explanations  of  the  bibliometric  results  in  terms  of  factors   relevant  to  policy.” • Commercialization Moedet  al.  (1985,  p.  131)
  • 19. Bibliometrics Research  evaluation • Part  of  hiring,  promotion  and  funding decisions • Dashboard  tools
  • 20. Bibliometrics Hvistendahl,  M.  (2013).  China’s  publication  bazaar.  Science,  342(6162),  1035-­‐1039. van  Noorden,  R.  (2013).  Brazilian  citation  scheme  outed:  Thomson  Reuters  suspends  journals  from  its  rankings  for  ‘citation  stacking’,  Nature,  500(7464),  510-­‐511.   Research  evaluation • Oversimplification • Publications  =  productivity • Citations  =  impact • Uninformed  use  and  misuse • Impact  factor • h-­‐index • Adverse  effects • “Salami”  publishing • Honorary  authorship • Self-­‐citations • Citation  cartels
  • 21. Scholarly metrics Björneborn,  L.  &  Ingwersen,  P.  (2004),  Toward  a  basic  framework  for  webometrics.  Journal  of  the  American  Society  for  Information  Science  and  Technology,   55(14),  1216–1227. Definitions informetrics scientometrics bibliometrics cybermetrics webometrics adaptedfrom:  Björneborn&  Ingwersen(2004,  p.  1217)
  • 22. Scholarly metrics Otlet,  P.  (1934).  Traité  de  documentation:  le  livre  sur  le  livre,  théorie  et  pratique. Pritchard,  P.  (1927).  Statistical bibliography or  bibliometrics?  Journal  of  Documentation,  25,  348-­‐349.. Bibliometrics informetrics scientometrics bibliometrics cybermetrics webometrics “La  «Bibliometrie»  sera   la  partie  définie  de  la   Bibliologie  qui  s'occupe   de  la  mesure  ou   quantité  appliquée  aux   livres.”   “the  application  of   mathematics  and   statistical  methods  to   books  and  other  media   of  communication” Pritchard  (1969,  p.  348) Otlet  (1934,  p.  14)
  • 23. Scholarly metrics Altmetrics adaptedfrom:  Björneborn&  Ingwersen(2004,  p.  1217) informetrics scientometrics bibliometrics cybermetrics webometrics altmetrics Björneborn,  L.  &  Ingwersen,  P.  (2004),  Toward  a  basic  framework  for  webometrics.  Journal  of  the  American  Society  for  Information  Science  and  Technology,   55(14),  1216–1227.
  • 24. Scholarly metrics Priem,  J.  (2014).  Altmetrics.  In  B.  Cronin  &  C.  R.  Sugimoto (Eds.),  Beyond  bibliometrics:  harnessing multidimensional indicators of  performance  (pp.  263–287).   Cambridge,  MA:  MIT  Press.   Rousseau,  R.  &  Ye,  F.  (2013).  A  multi-­‐metric approach for  research evaluation.  Chinese Science  Bulletin,  3288–3290.  doi:10.1007/s11434-­‐013-­‐5939-­‐3 Altmetrics informetrics scientometrics bibliometrics cybermetrics webometrics altmetrics “study  and  use  of   scholarly  impact   measures  based  on   activity  in  online  tools   and  environments” “a  good  idea  but  a   bad  name” Rousseau  &  Ye(2013,  p.  3289) Priem(2014,  p.  266)
  • 25. Scholarly metrics Definition altmetrics informetrics scientometrics bibliometrics cybermetrics webometrics adaptedfrom:  Björneborn&  Ingwersen(2004,  p.  1217) Scholarly  metrics Björneborn,  L.  &  Ingwersen,  P.  (2004),  Toward  a  basic  framework  for  webometrics.  Journal  of  the  American  Society  for  Information  Science  and  Technology,   55(14),  1216–1227.
  • 26. Scholarly metrics Haustein,  S.,  (2016).  Grand  challenges  in  altmetrics:  heterogeneity,  data  quality  and  dependencies.  Scientometrics,  108(1),  413–423.   altmetrics informetrics scientometrics bibliometrics cybermetrics webometrics Scholarly  metrics Acts:  viewing,  reading,   saving,  diffusing,   mentioning,  citing,   reusing,  modifying,  etc. Scholarly  documents:   papers,  books,  blog  posts,   datasets,  code,  etc. Scholarly  agents:   researchers,  universities,   funders,  journals,  etc. “[S]cholarly metrics  are   thus  defined  as  indicators   based  on  recorded  events   of  acts  […]  related  to   scholarly  documents  […]   or  scholarly  agents  […].” Haustein  (2016,  p.  348)
  • 27.
  • 28. Altmetrics Priem,  J.,  Taraborelli,  D.,  Groth,  P.,  &  Neylon,  C.  (2010).  Alt-­‐metrics:  a  manifesto.  October.  Retrieved from http://altmetrics.org/manifesto/   • Information  overload “We  rely  on  filters  to  make  sense  of  the  scholarly  literature,  but   the  narrow,  traditional  filters  are  being  swamped.  However,  the   growth  of  new,  online  scholarly  tools  allows  us  to  make  new   filters;  these  altmetrics  reflect  the  broad,  rapid  impact  of   scholarship  in  this  burgeoning  ecosystem.” • Criticism  against  current  form  of  research   evaluation • Alternative  forms  of  research  output • Alternative  use  and  visibility  of  publications Priem et  al.  (2010)
  • 29. Altmetrics Coverage per  platform Haustein,  S.,  Costas,  R.,  &  Larivière,  V.  (2015).  Characterizing  social  media  metrics  of  scholarly  papers:  The  effect  of  document  properties  and  collaboration   patterns.  PLoS ONE,  10(5),  e0127830.  doi:  10.1371/journal.pone.0120495 Zahedi,  Z.,  &  Haustein,  S.  (in  preparation).  Which  document  features  attract  users  in  Mendeley?  An  analysis  of  bibliographic  characteristics  of  Web  of  Science   publications  and  Mendeley  readership  counts.
  • 30. Mathematics  & Computer  Science Natural  Sciences &  Engineering Life  & Earth Sciences Biomedical & Health Sciences Social  Sciences &  Humanities 76,4  % 83,7  % 91,4  % 86,5  % 81,7  % Mendeley 7,5  % 12,9  % 21,6  % 31,7  % 26,0  % Twitter Altmetrics Coverage per  discipline Haustein,  S.,  Costas,  R.,  &  Larivière,  V.  (2015).  Characterizing  social  media  metrics  of  scholarly  papers:  The  effect  of  document  properties  and  collaboration   patterns.  PLoS ONE,  10(5),  e0127830.  doi:  10.1371/journal.pone.0120495 Zahedi,  Z.,  &  Haustein,  S.  (in  preparation).  Which  document  features  attract  users  in  Mendeley?  An  analysis  of  bibliographic  characteristics  of  Web  of  Science   publications  and  Mendeley  readership  counts.
  • 31. Altmetrics Density and  intensity per  platform Intensity Haustein,  S.,  Costas,  R.,  &  Larivière,  V.  (2015).  Characterizing  social  media  metrics  of  scholarly  papers:  The  effect  of  document  properties  and  collaboration   patterns.  PLoS ONE,  10(5),  e0127830.  doi:  10.1371/journal.pone.0120495 Zahedi,  Z.,  &  Haustein,  S.  (in  preparation).  Which  document  features  attract  users  in  Mendeley?  An  analysis  of  bibliographic  characteristics  of  Web  of  Science   publications  and  Mendeley  readership  counts.
  • 32. Altmetrics Spearman  correlations with citations Perfectnegativecorrelation Perfectpositive  correlation Haustein,  S.,  Larivière,  V.,  Thelwall,  M.,  Amyot,  D.,  &  Peters,  I.  (2014).  Tweets  vs.  Mendeley  readers:  How  do  these two social  media  metrics differ.   Information  Technology,  56(5),  207–215.  doi:  10.1515/itit-­‐2014-­‐1048 Haustein,  S.,  Costas,  R.,  &  Larivière,  V.  (2015).  Characterizing  social  media  metrics  of  scholarly  papers:  The  effect  of  document  properties  and  collaboration   patterns.  PLoS ONE,  10(5),  e0127830.  doi:  10.1371/journal.pone.0120495
  • 33. Altmetrics Spearman  correlations with citations NSF  Subdiscipline General  Biomedical Research 2011 Size of data points represents number of Mendeley readers in Twitter graph (left) and number of tweets in Mendeley graph (right). Haustein,  S.,  Larivière,  V.,  Thelwall,  M.,  Amyot,  D.,  &  Peters,  I.  (2014).  Tweets  vs.  Mendeley  readers:  How  do  these two social  media  metrics differ.   Information  Technology,  56(5),  207–215.  doi:  10.1515/itit-­‐2014-­‐1048
  • 34. Altmetrics Document  types Haustein,  S.,  Costas,  R.,  &  Larivière,  V.  (2015).  Characterizing  social  media  metrics  of  scholarly  papers:  The  effect  of  document  properties  and  collaboration   patterns.  PLoS ONE,  10(5),  e0127830.  doi:  10.1371/journal.pone.0120495
  • 40. Opportunities and  Challenges • Heterogeneity • Time  and  timing • Audiences  and  user  groups Altmetrics
  • 41. Opportunities • Different acts • Diverse  motivations Ø Diverse  impact Challenges • Understanding underlying processes Ø Determining the  meaning of  metrics Heterogeneity of  Altmetrics
  • 44. Heterogeneity Bertin,  M.,  Atanassova,  I.,  Gingras,  Y.,  &  Larivière,  V.  (2015).  The  invariant  distribution  of  references  in  scientific  articles.  Journal  of  the  Association  for     Information  Science  and  Technology, 67(1),  164-­‐177.  doi:  10.1002/asi.23367 Distribution  of  referencesalongthe  IMRaDstructure Citing in  a journal  article
  • 45. Heterogeneity Acts referring to  research objects Haustein,  S.,  Bowman,  T.  D.,  &  Costas,  R.  (2016).  Interpreting “altmetrics”:  Viewing acts on  social  media  through the  lens of  citation  and  social  theories.  Dans   C.  R.  Sugimoto (dir.),  Theories of  Informetrics and  Scholarly Communication  (p.  372–405).  Berlin:  De  Gruyter Mouton.  doi:  10.1515/9783110308464-­‐022 RESEARCH  OBJECT
  • 46. Time  and  Timing Opportunities • Detailed life  cycle  of  scientific output Ø Fine-­‐grained indicators and  adequate benchmarks Challenges • Versions  of  research output • Publication  dates
  • 47. Time  and  Timing Journal  article • Submitted manuscript • Revised manuscript • Accepted manuscript • Version  of  Record • Online  publication • Journal  issue • Online  date • Issue  month Ø Adjusting indicators 3  March  2014 15  July  2014 21  January2015 February2015
  • 49. Time  and  Timing Tweets  before publication?
  • 50. Time  and  Timing Weekday effects on  Twitter weekday of online publication: based on: 8,765 Springer papers with online publication date 19,010 tweets received within one year of online publication date
  • 51. Audiences  and  User  Groups Opportunities • Differentiating between types  of  users • Measuring societal impact Challenges • Identifying users and  user  groups • Determining engagement
  • 52. Audiences  and  User  Groups Alperin,  J.  P.  (2015).  Moving  beyond  counts:  A  method  for  surveying  Twitter  users.  In  altmetrics15:  5  years  in,  what  do  we  know?  Amsterdam,   The  Netherlands.  Retrieved  from:  http://altmetrics.org/wp-­‐content/uploads/2015/09/altmetrics15_paper_3.pdf Tsou,  A.,  Bowman,  T.  D.,  Ghazinejad,  A.,  &  Sugimoto,  C.  R.  (2015).  Who tweets  about  science?  In  Proceedings of  the  2015  International  Society  for   Scientometrics and  Informetrics (pp.  95–100).  Istanbul,  Turkey. Identifying Twitter  users • Altmetric.com  classification • Among  a  random  sample  of  2,000  accounts  tweeting   papers,  34%  of  individuals  identified  as  having  PhD • Of  286  users  linking  to  SciELO articles,  24%  employed  at   university,  23%  students,  36%  not  university  affiliated *based on  Altmetric.com  data  06/2015 (Tsou,  Bowman,  Ghazinejad,  &  Sugimoto,  2010) (Alperin,  2015)
  • 53. 1 2 3 Audiences  and  User  Groups Haustein,  S.,  &  Costas,  R.,  (2015).  Identifying Twitter  audiences:  who is tweeting about  scientific papers?  Communication  présentée  au  SIG/MET  Workshop,   ASIS&T  2015  Annual Meeting,  7  novembre  2015,  Saint-­‐Louis,  MO  (USA). topics  and collectives academic personal Node size number of  accounts associated with term Node color cluster  affiliation Terms  in  Twitter  bio
  • 54. Audiences  and  User  Groups Engagement  with scientific papers on  Twitter Haustein,  S.,  Bowman,  T.  D.,  Holmberg,  K.,  Tsou,  A.,  Sugimoto,  C.  R.,  &  Larivière,  V.  (2016).  Tweets  as  impact  indicators:  Examining  the  implications  of   automated  bot  accounts  on  Twitter.  Journal  of  the  Association  for  Information  Science  and  Technology,  67(1),  232–238.  doi:  10.1002/asi.23456
  • 55. Audiences  and  User  Groups Twitter  arXiv bots
  • 56. Audiences  and  User  Groups Journal  accounts
  • 57.
  • 58. Conclusions • Scholarly communication  and  the  reward system   of  science  are  changing • Potential to  become more  transparent  and   diverse • Open  Science • Scholarly metrics • Fundamental difference between posting on   social  media  and  academic publishing • More  metrics =  more  complexity
  • 59. Outlook More  scientometric studies. More  sociological research. Avoid adverse  effects.