INTRODUCTION: It is imperative that scholars investigate social media sites because their policies, configurations, designs, and affordances (the perception of functional attributes of objects by an agent in its environment (Gibson, 1977)) are constantly evolving in order to meet the needs of both their users and investors. The focus of this work is to explore the way in which scholars make use of affordances differently when they are creating personal and professional (as categorized by Amazon Turkers) messages in the microblogging site Twitter. This is important because social media is having an impact on the once invisible backstage activity of scholars, as Priem (Priem, 2014, p. 264) argues, by bringing “the background of scholarship… out onto the [front] stage.” Twitter, the 9th most visited website in the world, claims over 200 million active users who create over 400 million tweets each day (Wickre, 2013); it is a “global platform for public self-expression and conversation in real time” (Twitter Inc., 2013). Research has shown that approximately 10% to 30% of scholars (Priem, Costello, & Dzuba, 2011; Pscheida, Albrecht, Herbst, Minet, & Köhler, 2013; Rowlands, Nicholas, Russell, Canty, & Watkinson, 2011) make use of Twitter. This social media site presents a variety of unique affordances with which users can create tweets; these include the ‘@’ at symbol used to address messages to particular users (i.e. @user), the retweet feature (i.e., RT @user), the ‘#’ hashtag symbol (#ASIST) to group tweets and to search for tweets, the ability to add URLs, and the ability to add links to media (such as images and video). It is important to examine the way in which these affordances are used because they can influence the way in which the audience frames (Goffman, 1974) and interprets the tweet. This work is guided by the following questions: • Which affordances are scholars using? • Do personal or professional tweets vary regarding affordance use? • To what extent do scholars use affordances? • Does Twitter activity influence affordance use? METHODS: The personal Twitter streams of 445 scholars were downloaded in May 2014 using the Twitter API. The sample was derived from a selection of assistant, associate, full and distinguished professors across eight disciplines (Physics, Biology, Chemistry, Computer Science, Philosophy, English, Sociology, and Anthropology) from 62 Association of American University member universities. In sum, the scholars published a total of 585,879 tweets from 2006 to 2014. The sample of collected tweets from May 2014 totals 289,934 and the amount of tweets retrieved per user ranged between 1 and 3,263. A random subset of 75,000 tweets was placed into Amazon’s Mechanical Turk (AMT) application and three Turkers categorized each tweet into one of four categories: personal, professional, non-English, and unknown.

1. Timothy D. Bowman, Ph.D. Candidate | 2014 ASIST SIG/MET Workshop, Seattle, WA, USA

2. CRC.EBSI.UMONTREAL.CA
WHAT ARE AFFORDANCES?
• affordance - derived from afford,
meaning to make available or provide
naturally (Merriam-Webster, n.d.)
• Gibson (1977) affordance is the
perception of functional attributes of
objects by an agent in its environment
• affordances can vary depending both
on the context (time & space) they are
observed and by the agent doing the
observing
Figure 5: Tree affordance to bird, person, monkey,
and squirrel

3. AFFORDANCES AND SOCIAL MEDIA
• groups gain experience in digital contexts with
affordances and norms develop that enable interaction
(Bradner, 1999)
• feedback loop of personal and social use of affordances
creates consistent behaviors (Chalmers, 2004)
• interaction is moving from space-time constraints to
affordance-based constraints (Hogan, 2008)
• architecture of a particular environment matters; social
media architecture is shaped by their affordances (boyd,
2010)
CRC.EBSI.UMONTREAL.CA

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, 2013) or
CRC.EBSI.UMONTREAL.CA
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
AFFORDANCES IN TWITTER
Twitter claims over 200 million active users who create over
400 million tweets each day (Wickre, 2013);
The four widely known affordances in Twitter are:
• @ mention– used to mention, direct messages at, and/or to
reply to user(s)
• # hashtag – used to contextualize or categorize the message
• URL link – used to connect tweet to another information
source
• ReTweet (RT) – used to resend another's tweet

6. SCHOLARS USING TWITTER
- 43% scholars at 2012 STI Conference used
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)
CRC.EBSI.UMONTREAL.CA

7. CRC.EBSI.UMONTREAL.CA
RESEARCH QUESTIONS
1. Which affordances are scholars using?
2. Do personal or professional tweets vary
regarding affordance use?
3. To what extent do scholars use
affordances?
4. Does Twitter activity influence
affordance use?

8. CRC.EBSI.UMONTREAL.CA
PHASE ONE: SURVEY
- 16,862 scholars - associate, assistant, and full professors
from 62 AAU-member universities
- in physics, biology, chemistry, computer science, philosophy,
English, sociology, or anthropology departments
- 60 of the 62 universities rank in top 125 of 2014 CWTS Leiden
Ranking
- survey sent January and February 2014 with a response rate
of 8.5%
- 32% (613) reported having at least one Twitter account
- 289,934 tweets of 585,879 from 445 Twitter accounts (391
scholars) were found and harvested

9. PHASE ONE: 1,910 RESPONDENTS W/TWITTER ACCOUNT ARE:
33%
29%
40%
25%
29%
50%
28%
60%
50%
40%
30%
20%
10%
0%
American
Indian /
Native
American
(n=6)
Asian
(n=79)
Black /
African
American
(n=52)
Hispanic /
Latino
(n=40)
White /
Caucasian
(n=1580)
Pacific
Islander
(n=2)
Other
(n=50)
by ETHNICITY
38%
45%
38%
34% 36%
30%
27%
20%
16%
5%
2%
50%
40%
30%
20%
10%
0%
By SCHOLAR AGE
28% 28%
37% 37%
21%
50%
29%
24%
60%
50%
40%
30%
20%
10%
0%
by ACADEMIC DEPT
43%
36%
39%
41%
25%
40%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Less than 1
Year (n=68)
1 to 3 Years
(n=151)
4 to 6 Years
(n=144)
6 to 9 Years
(n=196)
10 Years of
More
(n=1262)
Not
Academic
(n=5)
by ACADEMIC AGE

10. PHASE ONE: WHO MAKES UP THE 613 ACCOUNT HOLDERS?
5%
10% 10%
15%
59%
Less than
1 Year
1 to 3
Years
4 to 6
Years
6 to 9
Years
10 Years
of More
7%
15%
5%
24%
17%
6%
10%
15%
Anthropology (n=49)
Biology (n=101)
Chemistry (n=35)
Computer Science (n=160)
42%
45%
40%
35%
30%
25%
20%
15%
10%
5%
22%
Personal Both Professional
35% 28%
19%
55%
44%
25%
33%
49%
39%
37% 60%
21%
25%
41%
24% 29% 26%
34%
22% 24% 31% 34%
by ACADEMIC DEPT
by PROFESSIONAL TITLE
by ACADEMIC AGE
SELF-REPORT
29% 29%
42%
0%
Assistant
Professor
Associate
Professor
Professor

11. PHASE ONE: MEAN TPD OF 391 SCHOLARS
by GENDER by DEPARTMENT
1.06
0.53
1.96
1.41
0.67
0.52
0.73
1.18
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. PHASE TWO: CATEGORIZATION IN AMT
- scholars were divided into 10 groups based on their mean TPD
- stratified sample of 75,000 tweets from these 10 groups
GROUP 1: 0 < 0.5 | GROUP 2: 0.5 < 1 | GROUP 3: 1 < 1.5 | GROUP 4: 1.5 < 2 | GROUP 5: 2 < 2.5
GROUP 6: 2.5 < 3 | GROUP 7: 3 < 4 | GROUP 8: 4 < 5 | GROUP 9: 5 < 8 | GROUP 10: > 8
- six random tweets were combined with a control question for a total of
CRC.EBSI.UMONTREAL.CA
12,056 AMT HITs
- three turkers were asked to categorize each tweet as either:
Personal for example using incomplete thoughts/sentences, profanity, everyday
events/language, personal opinions, excessive punctuation, informal
Professional for example using academic/scientific/business language or subjects,
correct punctuation, mention job title, referencing professional
organization, formal
Unknown from the text it is impossible to categorize as personal or professional
Non-English the text is not written in English

13. PHASE TWO: PERSONAL TWEETS CORRELATION TABLE

14. PHASE TWO: PROFESSIONAL TWEETS CORRELATION TABLE

15. PHASE TWO: PERSONAL & PROFESSIONAL TWEETS WITH AFFORDANCES
67%
69%
PARTIAL AGREEMENT (2/56%
AGREEMENT (3/3)
37%
28%
15% 17% 17%
66%
70%
60%
50%
40%
30%
20%
10%
0%
Mentions URLs Hashtags Retweets
Personal Tweets Professional Tweets
65%
38%
24%
30%
61% 62%
27%
38%
70%
60%
50%
40%
30%
20%
10%
0%
Mentions URLs Hashtags Retweets
Personal Professional
23%
AGREEMENT + PARTIAL
20% 22%
59%
65%
28%
38%
70%
60%
50%
40%
30%
20%
10%
0%
Mentions URLs Hashtags Retweets
Personal Professional
AGREEMENT
Personal Tweets: 27,264
Professional Tweets: 6,810
PARTIAL AGREEMENT
Personal Tweets: 19,403
Professional Tweets: 15,692
DISAGREEMENT
Personal Tweets: 942
Professional Tweets: 833

16. PHASE TWO: FREQUENCY OF AFFORDANCES USED IN PERSONAL & PROFESSIONAL TWEETS
1.38
AGREEMENT (3/3)
1.02
1.29
1.43
1.03
1.46
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Mentions URLs Hashtags
Personal Professional
1.48
1.45 1.40
1.03
1.03
1.47
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Mentions URLs Hashtags
Personal Professional
1.41
1.03
1.34
1.44
1.03
1.47
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Mentions URLs Hashtags
Personal Professional
PARTIAL AGREEMENT (2/3)
AGREEMENT + PARTIAL AGREEMENT
Personal Tweets: 27,264
Professional Tweets: 6,810
PARTIAL AGREEMENT
Personal Tweets: 19,403
Professional Tweets: 15,692
DISAGREEMENT
Personal Tweets: 942
Professional Tweets: 833

17. PHASE TWO: FREQUENCY OF AFFORDANCE USE BY IV GROUP
0.6
0.5
0.4
0.3
0.2
0.1
0
GROUP 1: 0 < 0.5 | GROUP 2: 0.5 < 1 | GROUP 3: 1 < 1.5 | GROUP 4: 1.5 < 2 | GROUP 5: 2 < 2.5
GROUP 6: 2.5 < 3 | GROUP 7: 3 < 4 | GROUP 8: 4 < 5 | GROUP 9: 5 < 8 | GROUP 10: > 8
Hashtags
1 2 3 4 5 6 7 8 9 10
Personal
Professional
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
URLs
1 2 3 4 5 6 7 8 9 10
Personal
Professional
1.4
1.2
1
0.8
0.6
0.4
0.2
0
User Mentions
1 2 3 4 5 6 7 8 9 10
Personal
Professional
13%
11%
15%
7%
% Retweets
8% 8% 8% 8%
9%
15%
22%
10%
17%
9%
8%
9%
7%
5%
9%
5%
25%
20%
15%
10%
5%
0%
1 2 3 4 5 6 7 8 9 10
Personal
Professional

18. PHASE TWO: FREQUENCY OF AFFORDANCE USE BY IV GENDER
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Hashtags
Female Male
Professional
Personal
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
URLs
Female Male
Professional
Personal
0.95
0.9
0.85
0.8
0.75
0.7
User Mentions
Female Male
Professional
Personal
23%
% Retweets
68%
23%
66%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Female Male
Personal
Professional

19. PHASE TWO: FREQUENCY OF AFFORDANCE USE BY IV DEPARTMENT
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Hashtags
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
Personal
Professional 0.00
0.20
0.10
URLs
Personal
Professional
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
User Mentions
Personal
Professional
7%
11%
2%
18%
33%
7% 7%
14%
5%
20%
2%
23%
20%
5%
7%
17%
40%
35%
30%
25%
20%
15%
10%
5%
0%
% Retweets
Personal
Professional

20. CRC.EBSI.UMONTREAL.CA
SUMMARY
• scholars are making use of all the primary affordances of Twitter and there does seem to be
consistency in their practices
• gender, department affiliation, communication type, and time spent on Twitter seems to have a
small impact on affordance use
• URL use is different in personal and professional tweets; there are many more professional
tweets with URLs, but the frequency of URLs used is similar between personal and professional
tweets
• #hashtag use shows variation by department for both personal and professional tweets;
• #hashtag use shows an upward trend as tweet activity increases for professional tweets and a
downward trend for personal tweets as tweet activity increases;
• #hashtag use shows variation by department for both personal and professional tweets;
• #hashtag use shows an upward trend as tweet activity increases for professional tweets and a
downward trend for personal tweets as tweet activity increases;
• @user mentions vary by gender with males using much less mentions in professional tweets
than females
• @user mentions vary by gender with males using much less mentions in professional tweets
than females

21. CRC.EBSI.UMONTREAL.CA
ONGOING WORK
• validity for tweet categorization is being checked currently by
surveying 90 most active scholars using Twitter and asking
them to self-categorize their own tweets
• using linguistic tools, the text of 289,934 tweets will be used to
compare terms used in tweets with scholar’s article titles at the
level of the scholar and discipline
• social network analysis using mentions at the scholarly and
discipline levels
• analysis of particular affordance usage

22. CRC.EBSI.UMONTREAL.CA
THANK YOU FOR LISTENING
QUESTIONS?

23. REFERENCES
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of “BABBLE”: A Field Study of Chat in the Workplace. In ECSCW’99
(pp. 12–16). Copenhagen, Denmark: Kluwer Academic Publishers.
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4441-4_8
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.
danah boyd. (2010). "Social Network Sites as Networked Publics:
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Chalmers, M. (2004). A Historical View of Context. Computer
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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
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Understanding how Twitter is used to spread scientific messages. In
Web Science Conference. Raleigh, NC.
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Merriam-Webster Dictionary. In Free Merriam-Webster Dictionary.
Merriam-Webster: An Encyclopedia Britannica Company. Retrieved
from http://www.merriam-webster.com/dictionary/afford
Moran, M., Seaman, J., & Tinti-Kane, H. (2011). Teaching, learning,
and sharing: How today’s higher education faculty use social media.
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874/257. Accessed 2011 December 7.
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September 01, 2013, from https://blog.twitter.com/2013/celebrating-twitter7

24. APPENDIX: 62 AAU-MEMBER UNIVERSITIES
CRC.EBSI.UMONTREAL.CA
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

25. APPENDIX: 10 GROUPS OF TWEETERS
Group Name Mean Tweets/Day Total Tweets Percentage Required Member Totals
TEN 8 to 24 29,064 10.02% 9
NINE 5 to 8 25,863 8.92% 8
EIGHT 4 to 5 19,321 6.66% 6
SEVEN 3 to 4 24,532 8.46% 10
SIX 2.5 to 3 25,508 8.80% 10
FIVE 2 to 2.5 22,195 7.66% 10
FOUR 1.5 to 2 23,018 7.94% 13
THREE 1 to 1.5 43,831 15.12% 29
TWO 0.5 to 1 30,463 10.51% 33
ONE < 0.5 46,139 15.91% 317
289,934 100.00% = 75,000 445

26. APPENDIX:
DESIGN OF
AMT HIT