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

Cascades, Islands, or Streams? Time, Topic, and Scholarly Activities in

Humanities and Social Science Research

Indiana University, Bloomington, USAUniversity of Wolverhampton, UKUniversité de Montréal, Canada

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

#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

RQThis 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?

TweetRetweet or RT@usernameMessage (privat)#Hashtag

Discipline Researchers Tweets1 Tweets per researcher

Astrophysics 45 59,742 1,328 Biochemistry 45 40,128 892Digital humanities 51 89,106 1,747Economics 45 57,673 1,282History 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

METHODSFrom 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.

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.

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

RESULTS

Figure 1. Types of tweets by discipline (facet 1) Astrophysics Biochemistry Digital humanities Economics History of science

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

23.5

42

22 24.5 25

31.5

16.5

38

16

28.5

23.521.5 15.5

38

27

21.5 20 24.5 21.5 19.5

OtherLinksConversationsRetweets

Intercoder agreement in facet 1 was 99.2%

62-75% of retweets contained links

RESULTS

Figure 2. Scientific content of the tweets by discipline (facet 2)Astrophysics Biochemistry Digital humanities Economics History of science

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2333.5

22

6.5 7.5

22

13.5

12.551.5

8.5

25.5 24

31.5

16

26.5

29.5 2934

26

57.5

Clearly not scienceNot clearDiscipline relevantScholarly communication

Intercoder agreement in facet 2 was 68.9% and 0.587 with Cohen’s Kappa.

RESULTS

Figure 3. Scientific content of the tweets by communication type

Astrophysics Biochemistry Digital humanities Economics History of science0%

5%

10%

15%

20%

25%

30%

35%

40%

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

OtherLinksConversationsRetweets

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.

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.

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.

Kim HolmbergStatistical Cybermetrics Research GroupUniversity of Wolverhampton, UKK.Holmberg@wlv.ac.uk http://kimholmberg.fi @kholmber

AcknowledgementsThis 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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