1.
Disciplinary Differences in
Twitter Scholarly
Communication
Kim Holmberg1 and Mike Thelwall2
1 k.holmberg@wlv.ac.uk, http://kimholmberg.fi | 2 m.thelwall@wlv.ac.uk
School of Technology, University of Wolverhampton, UK
ISSI 2013, Vienna, Austria, 17/7/13

2.
Cascades, Islands, or Streams?
Time, Topic, and Scholarly Activities in
Humanities and Social Science Research
Indiana University, Bloomington, USA
University of Wolverhampton, UK
Université de Montréal, Canada

3.
Cascades, Islands, or Streams?
Integrate several datasets representing a
broad range of scholarly activities
Use methodological and data triangulation
to explore the lifecycle of topics within and
across a range of scholarly activities
Develop transparent tools and techniques
to enable future predictive analyses

4.
#Altmetrics is the
study and use of non-
traditional scholarly
impact measures that
are based on activity
in web-based
environments.
http://www.ploscollections.org/article/browse/issue/info%3Adoi%2F10.1371%2Fissue.pcol.v02.i19;jsessionid=70DF7B9AD8D7CE819F666E7791D4084E

5.
This research investigates how researchers in different
disciplines use Twitter for scholarly communication
with the following research questions:
1. What do researchers typically tweet about?
2. Are there disciplinary differences in the types of
tweet sent by researchers?

6.
Tweet
Retweet or RT
@username
Message (privat)
#Hashtag

7.
Discipline Researchers Tweets1 Tweets per
researcher
Astrophysics 45 59,742 1,328
Biochemistry 45 40,128 892
Digital humanities 51 89,106 1,747
Economics 45 57,673 1,282
History of science 42 58,414 1,391
Data was collected between 4 March 2012 and 16 October 2012
using Twitter’s API (with http://lexiurl.wlv.ac.uk/)
DATA
1) Twitter restricts the collection of tweets sent by users to approx. 3,200 tweets per username

8.
METHODS
From each discipline a random sample
of 200 tweets were classified by the
first author using a multifaceted
classification scheme. Of these 25%
were coded by another researcher.
In facet 1 the communication style was
classified and in facet 2 the scientific
content, or lack of it, was classified.

9.
FACET 1communication style
• Retweets were identified by the acronym RT or by some other
way that clearly indicated that the tweet was at least a partial
copy of a previous tweet.
• Conversational tweets were identified by @-sign followed by a
username and were not retweets.
• Tweets in the Links category were tweets that were neither
retweets nor conversational tweets but contained one or more
URLs.
• Other- all remaining tweets.

10.
FACET 2content
• The scholarly communication category contained tweets that
were clearly about research-related communication.
• Discipline-relevant tweets were clearly about the discipline
but not directly research related.
• Not clear was for tweets with no clear topic. The topic of the
tweets and the scientific content were unclear.
• Not about science and not about the discipline. Tweets
irrelevant to the discipline and research.
A conservative approach was taken in the coding

11.
RESULTS
Figure 1. Types of tweets by discipline (facet 1)
23.5
42
22 24.5 25
31.5
16.5
38
16
28.5
23.5
21.5 15.5
38
27
21.5 20
24.5 21.5 19.5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Astrophysics Biochemistry Digital humanities Economics History of science
Other
Links
Conversations
Retweets
Intercoder agreement in facet 1 was 99.2%

12.
RESULTS
Figure 2. Scientific content of the tweets by discipline (facet 2)
23
33.5
22
6.5 7.5
22
13.5
12.5
51.5
8.5
25.5 24
31.5
16
26.5
29.5 29
34
26
57.5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Astrophysics Biochemistry Digital humanities Economics History of science
Clearly not science
Not clear
Discipline relevant
Scholarly communication
Intercoder agreement in facet 2 was 68.9% and 0.587 with Cohen’s Kappa.

13.
RESULTS
Figure 3. Scientific content of the tweets by communication type
6.5
18
8.5
1 1
3
3.5
3
0 0.5
10
7
3
5 4.5
3.5
5
7.5
0.5 1.5
0%
5%
10%
15%
20%
25%
30%
35%
40%
Astrophysics Biochemistry Digital humanities Economics History of science
Other
Links
Conversations
Retweets

14.
LIMITATIONS
• The sample is based upon 42-51 researchers per discipline
 The disciplinary differences found may be due to the
sample of researchers rather than their disciplines.
• It may be easier to classify tweets in some disciplines
 Some disciplines have more specialist vocabularies
(e.g., astrophysics) and others discuss issues that are of
general interest to society (e.g., economics).
 While facet 1 is fairly straightforward, facet 2 was classified
conservatively so that clear evidence was needed for the
more scholarly categories.

15.
CONCLUSIONS
The results suggest that researchers tend to share more
links and retweet more than the average Twitter users in
some earlier research.
The results suggests that there may be significant
differences between disciplines in the extent to which their
active users use Twitter for scholarly communication.
It seems to be worrying (?) that some disciplines are
avoiding Twitter almost completely for scholarly
communication despite other disciplines evidently finding it
useful for this purpose.

16.
FUTURE
Comparisons between active and not-so-active Twitter
users and closer analysis of tweeting behavior.
Deeper analysis of the scientific tweets and possible
relationships between the tweets and citations.
Survey about the researchers’ own thoughts about how
they use and what they think about Twitter.

17.
Kim Holmberg
Statistical Cybermetrics Research Group
University of Wolverhampton, UK
K.Holmberg@wlv.ac.uk
http://kimholmberg.fi
@kholmber
Acknowledgements
This manuscript is based upon work supported by the international funding initiative Digging into Data. Specifically, funding comes from
the National Science Foundation in the United States (Grant No. 1208804), JISC in the United Kingdom, and the Social Sciences and
Humanities Research Council of Canada.
Danke für Ihre Aufmerksamkeit
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