1. Scholarly communication and evaluation: from bibliometrics to altmetrics Stefanie Haustein stefanie.haustein@umontreal.ca @stefhaustein crc.ebsi.umontreal.ca/sloan

2. Scholarly Communication peer-reviewed journals 1665: Journal de Sçavans Philosophical Transactions replace personal correspondences registration certification dissemination archiving “Little Science, Big Science” Derek J. de Solla Price (1963) exponential growth

3. Scholarly Communication citation analysis as retrieval tool to handle information overload “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.” citation analysis as evaluation method  oversimplification of scientific work and success publications = productivity | citations = impact  adverse effects Garfield, 1955, p. 108

4. Scholarly Communication digital revolution  electronic publishing acceleration, openness and diversification of scholarly output and impact  open access and open science altmetrics manifesto: Priem, Taraborelli, Groth and Neylon (2010) “No one can read everything. 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.”

5. Altmetrics Criticism against current form of research evaluation: peer-reviewed publications in scholarly journals as the only form of output that “counts” particularly against Journal Impact Factor citations as the only form of impact that “counts” Altmetrics as alternatives: including all research “products” similar but more timely than citations  predicting scientific impact different, broader impact than citations  measuring societal impact

6. Altmetrics alternative use and visibility of publications on social media: more traditional forms of use: alternative forms of research output  pragmatic development based on IT developments … … …

7. Definitions and terminology webometrics “Polymorphous mentioning is likely to become a defining feature of Web- based scholarly communication.” “There will soon be a critical mass of web-based digital objects and usage statistics on which to model scholars’ communication behaviors […] and with which to track their scholarly influence and impact, broadly conceived and broadly felt.” PLOS article level metrics (ALM) altmetrics “study and use of scholarly impact measures based on activity in online tools and environments” “a good idea but a bad name” Priem (2014, p. 266) Cronin, Snyder, Rosenbaum, Martinson & Callahan (1998, p.1320) Cronin (2005, p. 196) Rousseau & Ye (2013, p. 2)

8. Definitions and terminology informetrics scientometrics bibliometrics cybermetrics webometrics altmetrics adapted from: Björneborn & Ingwersen (2004, p. 1217)

9. Definitions and terminology adapted from: Björneborn & Ingwersen (2004, p. 1217) informetrics scientometrics bibliometrics cybermetrics webometrics social media metrics social media metrics Haustein, Larivière, Thelwall, Amyot & Peters (2014)

10. Definitions and terminology adapted from: Björneborn & Ingwersen (2004, p. 1217) informetrics scientometrics bibliometrics cybermetrics webometrics social media metrics social media metrics “Although social media metrics seems a better fit as an umbrella term because it addresses the social media ecosystem from which they are captured, it fails to incorporate the sources that are not obtained from social media platforms (such as mainstream newspaper articles or policy documents) that are collected (for instance) by Altmetric.com.“ Haustein, Bowman & Costas (2015, p. 3)

11. Definitions and terminology adapted from: Björneborn & Ingwersen (2004, p. 1217) informetrics scientometrics bibliometrics cybermetrics webometrics social media metrics scholarly metrics

12. Definitions and terminology adapted from: Björneborn & Ingwersen (2004, p. 1217) informetrics scientometrics bibliometrics cybermetrics webometrics social media metrics scholarly metrics scholarly metrics “[T]he heterogeneity and dynamicity of the scholarly communication landscape make a suitable umbrella term elusive. It may be time to stop labeling these terms as parallel and oppositional (i.e., altmetrics vs bibliometrics) and instead think of all of them as available scholarly metrics— with varying validity depending on context and function.“ Haustein, Sugimoto & Larivière (2015, p. 3)

13. Definitions and terminology Acts leading to (online) events used for metrics RESEARCH OBJECT Haustein, Bowman & Costas (2015)

14. Social media metrics: research Which social media metrics are valid impact indicators? What kind of impact do the various metrics reflect? What is the relationship between social media activity and bibliometric variables? Which content receive the most attention on the platforms? Who is engaging with scholarly material on social media sites? What are the motivations behind this use?

15. Prevalence: social media uptake social media activity around scholarly articles grows 5% to 10% per month (Adie & Roe, 2013) Mendeley and Twitter largest sources for mentions of scholarly documents: Mendeley 521 million bookmarks 2.7 million users 32% increase of users from 9/2012 to 09/2013 (Haustein & Larivière, 2014) Twitter 500 million tweets per day 230 million active users 39% increase of users from 9/2012 to 09/2013 ca. 10% of researchers in professional context

16. Prevalence: coverage Mendeley 93% of Science articles 2007 (Li, Thelwall & Giustini, 2012) 94% of Nature articles 2007 (Li, Thelwall & Giustini, 2012) 80% of PLOS journals papers 2003-2010 (Priem, Piwowar & Hemminger, 2012) 66% of PubMed/WoS papers 2010-2012 (Haustein et al., 2014a) 63% of WoS papers with DOIs 2005-2011 (Zahedi, Costas & Wouters, 2014) 47% of Social Science WoS papers 2008 (Mohammadi et al., 2014) 35% of Engineering & Techn. WoS papers 2008 (Mohammadi et al., 2014) 31% of Physics WoS papers 2008 (Mohammadi et al., 2014) 13% of Humanities WoS papers 2008 (Mohammadi & Thelwall, 2014) Twitter 2% of WoS papers with DOIs 2005-2011 (Zahedi, Costas & Wouters, 2014) 9% of PubMed/WoS 2010-2012 (Haustein et al., 2014b) 13% of WoS papers with DOIs July-December 2011 (Costas, Zahedi & Wouters, 2014) 22% of WoS papers with DOIs 2012 (Haustein, Costas & Larivière, 2015)

17. Prevalence: density Mean number of events per paper per document type WoS papers 2012 with DOI ( Haustein, Costas & Larivière, 2015

18. Prevalence: density / intensity Mean number of events per paper WoS papers with DOIs 2012 all papers / papers with at least one social media event 0.03 / 1.51 Blogs 0.78 / 3.65 Twitter 0.08 / 1.78 Facebook 0.01 / 1.66 Google+ 0.01 / 1.54 Mainstream media PubMed/WoS papers 2010-2012 6.43 / 9.71 Mendeley ( Haustein et al., 2014a) ( Haustein, Costas & Larivière, 2015)

19. Similarity: correlations Spearman correlations with citations WoS papers with DOIs 2012 all papers / papers with at least one social media event 0.124 / 0.191 Blogs 0.194 / 0.148 Twitter 0.097 / 0.167 Facebook 0.065 / 0.209 Google+ 0.083 / 0.199 Mainstream media PubMed/WoS papers 2011 0.386 / 0.456 Mendeley ( Haustein et al., 2014a) ( Haustein, Costas & Larivière, 2015)

20. Popularity: highly tweeted Highly tweeted Physics paper

21. Popularity: highly tweeted Highly tweeted paper

22. Popularity: highly tweeted Highly tweeted paper

23. Communities of attention Distinguishing between types of Twitter impact engagement = dissimilarity with paper title exposure = number of followers

24. Communities of attention 660,149 original tweets (Altmetric.com up to June 2014) 237,222 tweeted documents (WoS 2012 with DOI) 125,083 unique users number of tweets to 2012 papers mean tweets per day (all tweets up to April 2015) mean relative citation rate of tweeted papers mean engagement (dissimilarity between tweet and paper title) mean exposure (mean number of followers during tweet) mean number of followers (April 2015) mean number of following (April 2015) tweeted document coupling user network ( Haustein, Bowman & Costas, submitted)

25. Communities of attention exposure engagement median dissimilarity with paper title median number of followers influencers / brokers orators / discussing disseminators / mumblers broadcasters tweet text differs from paper title tweet text is identical to paper title few followers many followers

26. Communities of attention number of users N = 125,083 mean tweets to papers tp = 5.3 mean tweets per day tpd = 5.9 mean relative citation rate mncs = 2.3 mean engagement men = 53.3 mean exposure mex = 1,382.6 mean number of followers mnfers= 2,027.2 mean number of following mnfing= 855.6 exposure engagement N = 29,770 tp = 3.2 tpd = 10.1 mncs = 2.4 men = 74.2 mex = 2,876.9 mnfers= 4,177.3 mnfing = 1,327.1 N = 32,768 tp = 1.7 tpd = 1.8 mncs = 2.5 men = 75.8 mex = 82.7 mnfers = 191.4 mnfing = 259.0 N = 32,680 tp = 11.5 tpd = 9.4 mncs = 2.1 men = 32.7 mex= 2,511.2 mnfers = 3,396.8 mnfing = 1,497.3 N = 29,865 tp = 4.4 tpd = 1.7 mncs = 2.2 men = 30.3 mex = 84.6 mnfers = 178.0 mnfing = 267.4 ( Haustein, Bowman & Costas, submitted)

27. Communities of attention number of users N = 125,083 mean tweets to papers tp = 5.3 mean tweets per day tpd = 5.9 mean relative citation rate mncs = 2.3 mean engagement men = 53.3 mean exposure mex = 1,382.6 mean number of followers mnfers= 2,027.2 mean number of following mnfing= 855.6 exposure engagement N = 29,770 tp = 3.2 tpd = 10.1 mncs = 2.4 men = 74.2 mex = 2,876.9 mnfers= 4,177.3 mnfing = 1,327.1 N = 32,768 tp = 1.7 tpd = 1.8 mncs = 2.5 men = 75.8 mex = 82.7 mnfers = 191.4 mnfing = 259.0 N = 32,680 tp = 11.5 tpd = 9.4 mncs = 2.1 men = 32.7 mex= 2,511.2 mnfers = 3,396.8 mnfing = 1,497.3 N = 29,865 tp = 4.4 tpd = 1.7 mncs = 2.2 men = 30.3 mex = 84.6 mnfers = 178.0 mnfing = 267.4 ( Haustein, Bowman & Costas, submitted)

28. Communities of attention number of users N = 125,083 mean tweets to papers tp = 5.3 mean tweets per day tpd = 5.9 mean relative citation rate mncs = 2.3 mean engagement men = 53.3 mean exposure mex = 1,382.6 mean number of followers mnfers= 2,027.2 mean number of following mnfing= 855.6 exposure engagement N = 29,770 tp = 3.2 tpd = 10.1 mncs = 2.4 men = 74.2 mex = 2,876.9 mnfers= 4,177.3 mnfing = 1,327.1 N = 32,768 tp = 1.7 tpd = 1.8 mncs = 2.5 men = 75.8 mex = 82.7 mnfers = 191.4 mnfing = 259.0 N = 32,680 tp = 11.5 tpd = 9.4 mncs = 2.1 men = 32.7 mex= 2,511.2 mnfers = 3,396.8 mnfing = 1,497.3 N = 29,865 tp = 4.4 tpd = 1.7 mncs = 2.2 men = 30.3 mex = 84.6 mnfers = 178.0 mnfing = 267.4 ( Haustein, Bowman & Costas, submitted)

29. Communities of attention more than 100 tweeted papers 708 of 125,083 users (0.6%) 9 57 130 512 ( Haustein, Bowman & Costas, submitted)

30. Communities of attention 708 of 125,083 users (0.6%) more than 100 tweeted papers ( Haustein, Bowman & Costas, submitted)

31. Some conclusions citations, Mendeley readers and tweets reflect different kinds of impact on different social groups Mendeley seems to mirror use of broader but still academic audience, largely students and postdocs Twitter seems to reflect popularity among general public and represents mix of societal impact, scientific discussion and dissemination and buzz differences between disciplines, document types and age  reader counts and tweets cannot be directly compared without normalization

32. Some conclusions fundamental differences between social media metrics and citations: gatekeeping community engagement quantitative and qualitative research needed: determine biases and confounding factors identify user groups identify user motivations and types of use  meaning of social media metrics needs to be understood before they are applied to research evaluation

33. Some tips When using altmetrics: time biases apply: don’t use for old papers! most metrics only captured for DOIs: remember limitation! social media metrics do not replace citations: don’t substitute! social media metrics are heterogeneous: don’t blend! document type: don’t compare! disciplinary differences: don’t compare! not all events reflect use or impact: differentiate! motivations and confounding factors unknown: be careful!

34. Stefanie Haustein Thank you for your attention! Questions? stefanie.haustein@umontreal.ca @stefhaustein crc.ebsi.umontreal.ca/sloan Thank you for your attention! Questions? Obrigada! Special Issue “Social Media Metrics” Aslib Journal of Information Management 67(3) Early View: www.emeraldinsight.com/toc/ajim/67/3www.emeraldinsight.com/toc/ajim/67/3 Links to OA preprints: crc.ebsi.umontreal.ca/aslib/crc.ebsi.umontreal.ca/aslib/