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Timothy D. Bowman, Ph.D. Candidate | 2014 ASIST SIG/MET Workshop, Seattle, WA, USA
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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
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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)
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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
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)
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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
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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)
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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?
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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
PHASE ONE: 1,910 RESPONDENTS W/TWITTER ACCOUNT ARE:
33%29%
40%
25%29%
50%
28%
0%
10%
20%
30%
40%
50%
60%
AmericanIndian /Native
American(n=6)
Asian(n=79)
Black /African
American(n=52)
Hispanic /Latino(n=40)
White /Caucasian(n=1580)
PacificIslander
(n=2)
Other(n=50)
by ETHNICITY
38%
45%
38%34% 36%
30%27%
20%16%
5%2%
0%
10%
20%
30%
40%
50%
By SCHOLAR AGE
28% 28%
37% 37%
21%
50%
29%24%
0%
10%
20%
30%
40%
50%
60%
by ACADEMIC DEPT
43%
36%39%
41%
25%
40%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Less than 1Year (n=68)
1 to 3 Years(n=151)
4 to 6 Years(n=144)
6 to 9 Years(n=196)
10 Years ofMore
(n=1262)
NotAcademic
(n=5)
by ACADEMIC AGE
5%10% 10%
15%
59%
Less than1 Year
1 to 3Years
4 to 6Years
6 to 9Years
10 Yearsof More
PHASE ONE: WHO MAKES UP THE 613 ACCOUNT HOLDERS?
7%
15%
5%
24%
17%
6%
10%
15%
Anthropology (n=49)Biology (n=101)Chemistry (n=35)Computer Science (n=160)
42%
22%35% 28%
19%
55%44%
25%
33%
49%39%
37% 60%
21%25%
41%
24% 29% 26%34%
22% 24% 31% 34%
Personal Both Professional
by ACADEMIC DEPTby PROFESSIONAL TITLE
by ACADEMIC AGE
SELF-REPORT
29% 29%
42%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
AssistantProfessor
AssociateProfessor
Professor
PHASE ONE: MEAN TPD OF 391 SCHOLARS
1.06
0.53
1.96
1.41
0.670.52
0.73
1.18
by DEPARTMENTby GENDER
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
0.670.85
I'm Not 10 Yearsor More
7 to 9Years
4 to 6Years
1 to 3Years
Lessthan 1Year
by ACADEMIC AGE
N=2N=207
SD=2.4
N=53
SD=2.2
N=35
SD=2.6
N=39
SD=0.9
N=21
SD=1.1
0.92
0.98
1.03
Professor AssociateProfessor
AssistantProfessor
by PROFESSIONAL TITLE
N=116
SD=2.1
N=116
SD=1.7
N=156
SD=2.9
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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
- six random tweets were combined with a control question for a total of
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
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
PHASE TWO: PERSONAL TWEETS CORRELATION TABLE
PHASE TWO: PROFESSIONAL TWEETS CORRELATION TABLE
PHASE TWO: PERSONAL & PROFESSIONAL TWEETS WITH AFFORDANCES
67%
15% 17% 17%
56%
69%
28%
37%
0%
10%
20%
30%
40%
50%
60%
70%
Mentions URLs Hashtags Retweets
Personal Tweets Professional Tweets
AGREEMENT (3/3)
65%
38%
24%
30%
61% 62%
27%
38%
0%
10%
20%
30%
40%
50%
60%
70%
Mentions URLs Hashtags Retweets
Personal Professional
PARTIAL AGREEMENT (2/3)
66%
23%20% 22%
59%65%
28%
38%
0%
10%
20%
30%
40%
50%
60%
70%
Mentions URLs Hashtags Retweets
Personal Professional
AGREEMENT + PARTIALAGREEMENT
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
PHASE TWO: FREQUENCY OF AFFORDANCES USED IN PERSONAL & PROFESSIONAL TWEETS
1.38
1.02
1.291.43
1.03
1.46
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Mentions URLs Hashtags
Personal Professional
1.48
1.03
1.401.45
1.03
1.47
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Mentions URLs Hashtags
Personal Professional
1.41
1.03
1.341.44
1.03
1.47
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Mentions URLs Hashtags
Personal Professional
AGREEMENT (3/3)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
PHASE TWO: FREQUENCY OF AFFORDANCE USE BY IV GROUP
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10
Hashtags
Personal
Professional
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10
URLs
Personal
Professional
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4 5 6 7 8 9 10
User Mentions
Personal
Professional
13%
11%
15%
7%8% 8% 8% 8%
9%
15%
22%
10%
17%
9%8%
9%
7%5%
9%
5%
0%
5%
10%
15%
20%
25%
1 2 3 4 5 6 7 8 9 10
% Retweets
Personal
Professional
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
PHASE TWO: FREQUENCY OF AFFORDANCE USE BY IV GENDER
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Female Male
Hashtags
Professional
Personal
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Female Male
URLs
Professional
Personal
0.7
0.75
0.8
0.85
0.9
0.95
Female Male
User Mentions
Professional
Personal
23%
68%
23%
66%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Female Male
% Retweets
Personal
Professional
PHASE TWO: FREQUENCY OF AFFORDANCE USE BY IV DEPARTMENT
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
Hashtags
Personal
Professional 0.000.100.200.300.400.500.600.700.800.901.00
URLs
Personal
Professional
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
User Mentions
Personal
Professional
7%
11%
2%
18%
33%
7% 7%
14%
5%
20%
2%
23%20%
5%7%
17%
0%5%
10%15%
20%25%30%35%
40%
% Retweets
Personal
Professional
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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
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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
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THANK YOU FOR LISTENING
QUESTIONS?
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Bowman, T. D., Demarest, B., Weingart, S. B., Simpson, G. L.,
Lariviere, V., Thelwall, M., & Sugimoto, C. R. (2013). Mapping DH
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twitter7
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APPENDIX: 62 AAU-MEMBER UNIVERSITIES
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
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
APPENDIX:
DESIGN OF
AMT HIT