1. Timothy D. Bowman, Ph.D. Candidate | 19th Nordic Workshop on Bibliometrics and Research Policy
2. WHY INVESTIGATE SCHOLARLY ACTIVITY IN SOCIAL MEDIA ?
- New technology allows for reassessment
and reevaluation of academia (Baldwin,
1998)
- Social media use provides insight into
customs and traditions (Greenhow, 2009)
- Social media use unveiling once
invisible backstage activity (Priem, 2014)
CRC.EBSI.UMONTREAL.CA
3. HAVE WE MOVED “BEYOND BIBLIOMETRICS”?
- We’ve moved beyond simply measuring citations
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(Cronin & Sugimoto, 2014).
- New tools and data allow for new kinds of metrics
measuring wide array of indicators (Cronin, 2014)
- Electronic publishing magnifies a scholar’s
awareness of own performance (Wouters, 2014)
- Evaluation of bibliometric indicators by novices
allows for wide use of various ad hoc indicators
(Gingras, 2014)
4. WHY CONSIDER “ALTMETRICS” OR “INFLUMETRICS” OR
SIMPLY “SOCIAL MEDIA METRICS”?
- “Altmetrics” is the measure of scholarly communication
and dissemination within social media contexts (Priem &
Hemminger, 2010; Priem, Taraborelli, Groth & Neylon,
2010)
- Perhaps a better term is Influmetrics (Rousseau & Ye,
CRC.EBSI.UMONTREAL.CA
2013) or simply “social media metrics”?
- Social media indicators may measure immediate
assessment of academic impact and social impact
(Thelwall, Haustein, Larivière & Sugimoto, 2013)
- “Products,” not “publications” (Piwowar, 2013)
5. CRC.EBSI.UMONTREAL.CA
DO SCHOLARS USE TWITTER?
- 92% of Semantic Web scholars had Twitter account and
rated it as favorite for spreading scientific information
(Letierce, Passant, Decker, & Breslin, 2010)
- Total of 367 scholars reported increasing acceptance for
blogs and microblogs for consumption and
dissemination of scientific information (Gruzd, Goertzen,
& Mai, 2012)
- Scholars’ tweets tend to share information about (a)
professional discussions, (b) network with others, (c)
offer help / request help, (d) call attention to other social
media involvement, and (e) personal discussions, and (f)
impression management (Veletsianos, 2012)
6. CRC.EBSI.UMONTREAL.CA
DO SCHOLARS USE TWITTER? (CONT.)
- 43% scholars at 2012 STI Conference using
Twitter; it was used privately and professionally,
to distribute professional information, and to
improve visibility (Haustein et al., 2013)
- 80% DH scholars ranked Twitter as relevant for
consumption and 73% for dissemination of DH
information (Bowman et al., 2013)
- Differences by discipline found regarding the
way scholars used Twitter (Holmberg &
Thelwall, 2014)
7. CRC.EBSI.UMONTREAL.CA
RESEARCH QUESTIONS
1. What differences exist between the
tweeting behavior of scholars in the
natural and social sciences?
2. What kind of relationships exists
between tweeting and publication
behavior?
3. How does Twitter affordance use differ
across disciplines?
8. CRC.EBSI.UMONTREAL.CA
WHAT DATA IS IN THIS SAMPLE?
- 16,862 Associate, Assistant, and Full professors from webpages
at 62 AAU-member universities
- The faculty belonged to either Physics, Biology, Chemistry,
Computer Science, Philosophy, English, Sociology, or
Anthropology departments.
- 60 of the 62 universities rank in the top 125 according to 2014
CWTS Leiden Ranking
- Survey sent January and February 2014 with a response rate of
8.5% (1,910 responses)
- Of these responders, 32% (615) reported having at least one
Twitter account
- 289,934 tweets of 585,879 from 445 accounts (391 scholars)
were collected.
9. CRC.EBSI.UMONTREAL.CA
HOW WAS THE DATA COLLECTED?
- Twitter API, Local WoS Database, Manual
cleaning of authors
- Twitter:
- tweets, # of tweets, followers, friends, retweets,
created date
- affordances: @mention, #hashtag, URLs, media,
symbols, retweets
-WoS
- publications, citation averages
10. ALL 1,910 SURVEY RESPONDENTS :: HAVE TWITTER ACCOUNTS?
42.65%
by ACADEMIC AGE
36.42%
38.89%
40.82%
24.96%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Less than 1
Year
1 to 3 Years 4 to 6 Years 6 to 9 Years 10 Years of
More
I'm not
38.10%
45.09%
38.27%
34.31% 35.75%
29.68%
26.58%
19.81%
16.34%
60%
50%
40%
30%
20%
10%
5.26%
2.38%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
26 to 30
years
31 to 35
years
36 to 40
years
41 to 45
years
46 to 50
years
51 to 55
years
56 to 60
years
61 to 65
years
66 to 70
years
71 to 75
years
Over 75
years
by AGE
33.33%
29.11%
40.38%
25.00%
by ETHNICITY
29.11%
50.00%
28.00%
0%
American
Indian /
Native
American
Asian Black /
African
American
Hispanic /
Latino
White /
Caucasian
Pacific
Islander
Other
60% by DISCIPLINE
28.10% 27.52%
37.46% 36.90%
50.00%
20.71%
28.99%
23.64%
50%
40%
30%
20%
10%
0%
11. ONLY 391 SCHOLARS WITH TWITTER ACCOUNTS :: MEAN OF TWEETS PER DAY
1.06
0.53
1.96
by DEPARTMENT by GENDER
1.41
0.67
0.52
0.73
1.18
1.14
0.80
1.02
Other Female Male
N=232
SD=2.3
N=122
SD=2.1
N=3
0.89
1.11
1.39
0.67
0.85
I'm Not 10 Years
or More
7 to 9
Years
4 to 6
Years
1 to 3
Years
Less
than 1
Year
by ACADEMIC AGE
N=2
N=207
SD=2.4
N=53
SD=2.2
N=35
SD=2.6
N=39
SD=0.9
N=21
SD=1.1
by PROFESSIONAL TITLE
0.92
0.98
1.03
Professor Associate
Professor
Assistant
Professor
N=116
SD=2.1
N=116
SD=1.7
N=156
SD=2.9
12. BY DISCIPLINE :: RELATIONSHIP OF MEAN TWEETS PER DAY TO # OF ARTICLES
12
10
8
6
4
2
500
450
400
350
300
250
200
150
100
50
0
Anthropology
Physics
0 2 4 6 8
0
0 5 10 15
25
20
15
10
5
0
Philosophy
0 5 10 15 20 25
60
50
40
30
20
10
0
Sociology
0 5 10 15 20
Articles
N=40 N=30
N=66
Tweets per Day
N=19
16. MEAN PERCENTAGE OF TWEETS WITH AFFORDANCE PER PERSON BY DEPARTMENT
AND MEAN OF REWTEETS BY COLLECTED TWEETS
7.44%
6.41%
8%
7%
6%
5%
4%
3%
2%
1%
0%
HASHTAGS
Anthropology
Biology
Chemistry
Computer Science
English
Philosophy
Physics
Sociology
16.28%
20.06%
25%
20%
15%
10%
5%
0%
MENTIONS
Anthropology
Biology
Chemistry
Computer Science
English
Philosophy
Physics
Sociology
1.16%
0.72%
1.12%
0.25%
1.69%
0.53%
1.09%
0.77%
2%
1%
0%
URLs
Anthropology
Biology
Chemistry
Computer Science
English
Philosophy
Physics
Sociology
353
3291
3,500
3,000
2,500
2,000
1,500
1,000
500
0
RETWEETS
Anthropology
Biology
Chemistry
Computer Science
English
Philosophy
Physics
Sociology
17. CRC.EBSI.UMONTREAL.CA
SUMMARY
• As expected, the data reflected differences of those who reported
having Twitter accounts based on academic age and actual age.
• Of the 391 scholars (445 Twitter accounts) that were collected, the
data did reflect differences in mean tweets per day based on gender,
discipline, and academic age and title
• Finally, it was found that the data reflected no strong relationships
between mean tweets per day and publication output or
• There was no real relationship between average citations and mean
tweets per day (scholarly impact)
• The data did reflect small differences in affordance use by discipline,
especially the differences in retweets but theses differences in
retweets are not an accurate representation of the retweets by the
scholar
18. ONGOING WORK
• Further analysis of retweets needed attempting to focus solely on
retweets made by the scholars themselves
• Using linguistic tools, the text of the 289,934 tweets will be used to
compare terms used in tweets with article titles at the level of the
scholar and discipline
• A social network analysis will be completed reflecting the mentions
used in tweets at the scholarly and discipline levels
• A closer examination of the actual affordances (unique hashtags,
unique URLs, unique mentions) used
• A categorization of tweets as either personal or professional by
Turkers
• A general discussion on what these social media metrics are actually
measuring including any correlations between social media use and
publication activity
CRC.EBSI.UMONTREAL.CA
19. CRC.EBSI.UMONTREAL.CA
THANK YOU
This work was partially funded by a grant
by The Alfred P. Sloan foundation
and a Canada Research Chair grant
DO YOU HAVE ANY QUESTIONS?
20. REFERENCES
Baldwin, R. G. (1998). Technology’s Impact on Faculty Life and Work.
New Directions for Teaching and Learning, (76), 7–21.
doi:10.1002/tl.7601
Bowman, T. D., Demarest, B., Weingart, S. B., Simpson, G. L.,
Lariviere, V., Thelwall, M., & Sugimoto, C. R. (2013). Mapping DH
through heterogeneous communicative practices. In Digital
Humanities 2013. Lincoln, NE.
Cronin, B. (2014). Scholars and scripts, spoors and scores. In B.
Cronin & C. R. Sugimoto (Eds.), Beyond bibliometrics: Harnessing
multidimensional indicators of scholarly impact (pp. 3-22). Cambridge,
Mass.: MIT Press.
Cronin, B. & Sugimoto, C.R. (2014). Preface. In B. Cronin & C. R.
Sugimoto (Eds.), Beyond bibliometrics: Harnessing multidimensional
indicators of scholarly impact (pp. vii). Cambridge, Mass.: MIT Press.
Gingras, Y. (2014). Criteria for evaluating indicators. In B. Cronin & C.
R. Sugimoto (Eds.), Beyond bibliometrics: Harnessing
multidimensional indicators of scholarly impact (pp. 109-126).
Cambridge, Mass.: MIT Press.
Greenhow, C. (2009). Social scholarship: applying social networking
technologies to research practices. Knowledge Quest, 37(4), 42–47.
Retrieved from
http://aasl.metapress.com/index/r282223126950757.pdf
Gruzd, A., Goertzen, M., & Mai, P. (2012). Survey results highlights:
Trends in scholarly communication and knowledge dissemination (p.
10). Retrieved from http://socialmedialab.ca/?p=4308
Haustein, S., Peters, I., Bar-Ilan, J., Priem, J., Shema, H., &
Terliesner, J. (2013). Coverage and adoption of altmetrics sources in
the bibliometric community. arXiv, 1–12. Digital Libraries. Retrieved
from http://arxiv.org/abs/1304.7300
Holmberg, K., & Thelwall, M. (2014). Disciplinary differences in Twitter
scholarly communication. Scientometrics. doi:10.1007/s11192-014-
1229-3
Understanding how Twitter is used to spread scientific messages. In
Web Science Conference. Raleigh, NC.
Moran, M., Seaman, J., & Tinti-Kane, H. (2011). Teaching, learning,
and sharing: How today’s higher education faculty use social media.
Piwowar, H. (2013). Altmetrics: Value all research products. Nature,
493(159). doi:10.1038/493159a
Priem J., & Hemminger B.M. (2010) Scientometrics 2.0: Toward new
metrics of scholarly impact on the social web. First Monday 15.
Available:
http://firstmonday.org/htbin/cgiwrap/bin /ojs/index.php/fm/article/view/2
874/257. Accessed 2011 December 7.
Priem, J., Taraborelli, D., Groth, P., Neylon, C. Alt-metrics: a
manifesto. 2010. Available from http://altmetrics.org/manifesto/
Priem, J. (2014). Altmetrics. In B. Cronin & C. R. Sugimoto (Eds.),
Beyond bibliometrics: Harnessing multidimensional indicators of
scholarly impact (pp. 263–288). Cambridge, Mass.: MIT Press.
Rousseau, R., & Ye, F. (2013). A multi-metric approach for research
evaluation. Chinese Science Bulletin, 58(3290), 1–7.
doi:10.1007/s11434-013-5939-3
Thelwall M., Haustein S., Larivière V., Sugimoto, C.R. (2013) Do
Altmetrics Work? Twitter and Ten Other Social Web Services. PLoS
ONE 8(5): e64841. doi:10.1371/journal.pone.0064841
Veletsianos, G. (2012). Higher education scholars’ participation and
practices on Twitter. Journal of Computer Assisted Learning, 28(4),
336–349. doi:10.1111/j.1365-2729.2011.00449.x
Wouters, P. (2014). The citation: From culture to infrastructure. In B.
Cronin & C. R. Sugimoto (Eds.), Beyond bibliometrics: Harnessing
multidimensional indicators of scholarly impact (pp. 47–66).
Cambridge, Mass.: MIT Press.
21. APPENDIX: UNIVERSITY DISTRIBUTION
Yale University
Washington University in St. Louis
Vanderbilt University
University of Washington
University of Virginia
University of Toronto
University of Southern California
University of Rochester
University of Pittsburgh
University of Pennsylvania
University of Oregon
University of Missouri-Columbia
University of Minnesota
University of Michigan
University of Maryland
University of Illinois at Urbana-Champaign (1908)
University of Florida
University of Colorado Boulder
University of California, Santa Barbara
University of California, San Diego
University of California, Los Angeles
University of California, Irvine
University of California, Davis
University of California, Berkeley
University of Arizona
University at Buffalo, The State University of New York (1989)
Tulane University
The University of Wisconsin-Madison
The University of Texas at Austin
The University of North Carolina at Chapel Hill
The University of Kansas
The University of Iowa (1909)
The University of Chicago
The Pennsylvania State University (1958)
The Ohio State University (1916)
Texas A&M University
Stony Brook University-State University of New York (2001)
Stanford University (1900)
Rutgers, The State University of New Jersey (1989)
Rice University (1985)
Purdue University (1958)
Princeton University (1900)
Northwestern
New York University
MIT
Michigan State University (1964)
McGill
Johns Hopkins
Iowa State
Indiana University
Harvard
Georgia Institute of Technology
Emory University
Duke University
Cornell
Columbia University
Case Western Reserve University
Carnegie Mellon University
California Institute of Technology
Brown University
Brandeis University
Boston University
University of Maryland (3.32%)
University of Wisconsin-Madison (4.85%)
Indiana University (4.08%)
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%
22. APPENDIX: 62 AAU-MEMBER UNIVERSITIES
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Boston University, Brandeis University,
Brown University, California Institute of
Technology, Carnegie Mellon
University, Case Western Reserve
University, Columbia University,
Cornell, Duke University, Emory
University, Georgia Institute of
Technology, Harvard, Indiana
University, Iowa State, Johns Hopkins,
McGill, Michigan State University, MIT,
New York University, Northwestern,
Princeton University, Purdue
University, Rice University, Rutgers,
The State University of New Jersey,
Stanford University, Stony Brook
University-State University of New
York, Texas A&M University, The Ohio
State University, The Pennsylvania
State University, The University of
Chicago, Tulane University, University
at Buffalo, The State University of New
York, University of Arizona, University
of California, Berkeley, University of
California, Davis, University of
California, Irvine, University of
California, Los Angeles, University of
California, San Diego, and University of
California, Santa Barbara ,The
University of Iowa, The University of
Kansas, The University of North
Carolina at Chapel Hill, The University
of Texas at Austin, The University of
Wisconsin-Madison, University of
Colorado Boulder, University of
Florida, University of Illinois at Urbana-
Champaign, University of Maryland,
University of Michigan, University of
Minnesota, University of Missouri-
Columbia, University of Oregon,
University of Pennsylvania, University
of Pittsburgh, University of Rochester,
University of Southern California,
University of Toronto, University of
Virginia, University of Washington,
Vanderbilt University, Washington
University in St. Louis, Yale University
Notas del editor
We are living in what many view as the age of “big data”, thus some of us want to understand what the flood of data is telling us about the way we present ourselves online, how we disseminate and consume information, and how we acquire and make use of various types of capital in these contexts.
If we look at recent history beginning post-1996 and the advent of the graphical web, some interested in studying science and scholarly activity took a closer look at new technologies because they could see that it might allow for a reexamination of academia.
As the web evolved and scholars more frequently contributed to listservs, blogs, data repositories, microblogs, social network sites, and other types of social media, it became clear that the traces of scholarly activities in these new environments were valuable in that they could provide insight into the structure and norms of the academy and to previously invisible activities of scholars.
The construction of these various social media tools and the foundation (like programming languages and database systems) these tools are built upon allow us to more easily collect, store, retrieve, and evaluate the large amounts of data produced within these contexts.
These are just a few of the reasons that there has been an influx of research on scholarly activity in social media
Technological innovations and the evolution of these online social media tools has led many to look beyond the traditional metrics in an attempt to provide a better measure of scholarly impact.
The tools, platforms, and changing access to data has allowed for the identification of various metrics attempting to detect and measure novel types of indicators
In addition to the these tools, platforms and data access, the shift toward electronic publishing has enhanced the ability of scholars to measure their own performance and the performance of their colleagues; this phenomenon can create a desire for additional forms of metrics and indicators in order to further distinguish oneself from others.
Also, the ability to evaluate bibliometric indicators has moved from the domain to experts to the realm of novices who are using online tools employing hidden algorithms to report on their own academic impact
This influx of metrics used to evaluate online contexts has led some to label them as altmetrics, a concept defined as “the measure of scholarly communication and dissemination within social media contexts”
It seems that altmetrics is a term that no longer serves to adequately explain what it is that we are measuring because these indicators are not measuring phenomenon alternative to something else such as citations or journal impact, but instead measure the traces of activity in the context of social media and other tools that were once either unavailable or invisible.
Instead I think of these as simply social media metrics.
One of the appeals of the measure of social media indicators is that it might provide immediate insight into immediate academic and social impact; this has been compared to citations that both take a longer period of time to accumulate and only measure those who cite
Another reason social media metrics are important today is that organizations such as the National Science Foundation in the U.S. are stipulating that scholars submit a list of their “products,” not just a list of relevant “publications”, when applying for funding. This indicates that a scholar’s publications are no longer enough to determine productivity, impact and overall value.
These are just some of the reasons why social media metrics are an important and interesting area of research
What do we know about scholar’s use of social media in contexts such as Twitter?
Work examining this phenomena has focused on a variety of disciplines including one study that surveyed 61 Semantic Web scholars finding that 92% had a Twitter account and rated it as their favorite service to spread scientific information
In another work, a survey of 367 primarily social science scholars reported an increasing acceptance for blogs and microblogs as trustworthy and legitimate sources for the consumption and dissemination of scientific information
In a content analysis of scholarly tweets it was discovered that scholars tended to (a) share information about their professional practice, (b) attempt to network, (c) offer help and request help, (d) call attention to other social media involvement, (e) engage in personal discussions, and (f) iin impression management
Another survey of 71 scholars at the 2012 STI Conference found that 43% reported using Twitter and that they used it privately, professionally, to distribute professional information, and to improve their visibility
Over 200 Digital Humanities scholars surveyed with 80% reporting Twitter as relevant for consumption of DH and 73% reported it as relevant for dissemination of DH information (Bowman et al., 2013)
Finally, scholars from 10 different disciplines (astrophysics, biochemistry, digital humanities, economics, history of science, cheminformatics, cognitive science, drug discovery, social network analysis, and sociology) were analyzed and it was found that there were differences in the way they used Twitter (Holmberg & Thelwall, 2014)
For this work I was interested in three exploratory questions:
What differences exist exist across these humanities and natural science disciplines?
What kind of relationships exist between tweeting and publication behavior?
And finally, how does Twitter affordance use differ across disciplines?
Here I define an affordance as a relation between an object or environment (in this case a tweet) and an organism (human) that affords the opportunity for that organism to perform an action (such as categorizing a tweet with #, referencing someone using an @ symbol, or providing additional information with a URL).
Information of 16,862 Associate, Assistant, and Full professors from eight departmental webpages from 62 universities belonging to the Association of American Universities was harvested between September 2013 and January 2014.
The faculty belonged to either Physics, Biology, Chemistry, Computer Science, Philosophy, English, Sociology, or Anthropology.
According to the 2014 CWTS Leiden Ranking website that lists universities by scholarly impact, 60 of the 62 universities included in this sample rank in the top 125 of this ranking http://www.leidenranking.com/ranking/2014
A survey was sent to all of the faculty between January and February 2014 with a response rate of 8.5% (1,910 responses). A
Of these, 32% (615) reported having a Twitter account
Of the 615 scholars who reported having a Twitter account, 289,934 tweets of 585,879 from 445 accounts were collected. Note that the Twitter API restricts the collection of tweets the most recent 3,200 tweets per account.
The missing 170 accounts were either private or could not be found.
There were 41 scholars with 2 accounts, 11 scholars with three account, and 1 scholar with 5 --- leaving 391 scholars
Twitter API was used in combination with PHP and MySQL to collect and store the tweets from the 445 scholar accounts
Local WoS SQL Server database containing data provided by Thomson Reuters was searched with SQL to match author names with scholar names from the collected Twitter account
321,033 publication records retrieved from initial WoS search, author name disambiguation resulted in 7,734 articles across 391 authors
When a media
Using the data from the survey and comparing those who self-reported having a Twitter account with those who did not, we see that when comparing respondents by academic age there is a big drop off after 9 years.
There is no clear differences by ethnicity and we see that there is definitely a drop off by age, with those 31 to 35 years old reporting the most uses.
IAC Average is the average of citation impact of the person
Again, an affordance is defined as a relation between an object (tweet), or an environment, and an organism (human) that affords the opportunity for that organism to perform an action (categorize tweet with hashtag).
I want to give each researcher the same weight, so I want to look at the affordance use per person. The overall mean is XXX. This is what we expect the scholar to do… we see that…
The problem with this analysis is that there is what I consider a bug in the Twitter API; the giant number in physics reflected here is from the fact that one physicst retweeted the Ellen Degeneris oscar selfie tweet and Twitter counts the original tweets ‘retweet_count’ when it delivers the tweet count for each user. It seems to me it should provide the retweet count of the current Twitter user, but this is not the case.
In response to research question one which asked about relationship amongst scholarly Twitter use, there were differences found between scholars reporting having a Twitter account with a difference by academic age and actual age.
It was also found that there was differences in mean tweets per day by discipline, academic age, academic title, and gender with the caveat that there were high standard deviations.
With regards to question two thinking about any coorrelations between Twitter use and publication activity and Twitter use and scholarly impact, it was found that there were no real relationaships between these two activities. Scholars who tweeted frequently were not high publishers and tweet behavior did not seem to have an impact on scholarly impact
Finally, there were small differences found between departments regarding affordance use in Twitter, but further analysis is needed