Disciplinary Differences in Twitter Scholarly Communication
Kim Holmberg1 and Mike Thelwall2
1

k.holmberg@wlv.ac.uk | 2 m.thelwall@wlv.ac.uk
School of Technology, University of Wolverhampton
Wulfruna Street, Wolverhampton WV1 1LY, UK

Abstract
This paper investigates disciplinary differences in how researchers use the microblogging site Twitter. Tweets
from researchers in five disciplines (astrophysics, biochemistry, digital humanities, economics, and history ofscience) were collected and analyzed both statistically and qualitatively. The results suggest that researchers tend
to share more links and retweet more than the average Twitter users in earlier research. The results also suggestthat there are clear disciplinary differences in how researchers use Twitter. Biochemists retweet substantiallymore than researchers in the other disciplines. Researchers in digital humanities use Twitter more forconversations, while researchers in economics share more links than other researchers. The results also suggestthat researchers in biochemistry, astrophysics and digital humanities are using Twitter for scholarly
communication, while scientific use of Twitter in economics and history of science is marginal.

Conference Topic
Webometrics (Topic 7) and Old and New Data Sources for Scientometric Studies: Coverage, Accuracy andReliability (Topic 2)

Introduction
Scholarly communication is changing as researchers increasingly use social media to discover
new research opportunities, discuss research with colleagues and disseminate research
information. Traditionally, scholarly communication may be seen as a process that starts witha research idea and ends with a formal peer reviewed scientific publication. During thisprocess, ideas may be informally discussed with colleagues or presented at seminars andconferences and, after publication, the results may be read and formally cited by otherresearchers. With the advent of the web both formal and informal scholarly communication
has changed. Because of the web, ideas can be more easily and quickly discussed withcolleagues over email or video conferencing tools and articles can be published on the web ininstitutional repositories, online full text databases or online open access journals. Now it
seems that social media are triggering another evolution of scholarly communication.
Citations are important in scholarly communication. They are the link that connects earlier
research to new research. They indicate use of earlier research, and hence it can be argued thatthey indicate something about the value of the cited research. Citations are also part of theacademic reward system (Merton, 1968), with highly cited authors tending to be recognized
as having made a significant contribution to science. Counting citations is at the core ofscientometric methods; they have been used to measure scholarly work and intellectual
influence and to map collaboration networks between scholars (Moed et al., 1995; Cole, 2000;
Borgman, 2000). However, citations can be created for many different reasons (Borgman &Furner, 2002) and because both publishing and citation traditions vary between disciplines,
new ways are needed to measure the visibility and impact of research. In this context, social
media may generate new ways to measure scientific output (Priem & Hemminger, 2010).
Social bookmarking sites such as Connotea and CiteULike, or recommendation systems like
Reddit and Digg, may prove to be fruitful sources for new scientific visibility metrics (Priem& Hemminger, 2010). One of the new social media services that researchers can use inscholarly communication and that has some potential in providing new ways to measureresearch impact is Twitter.

Twitter is a real-time microblog network; users can publish their opinions, ideas, stories, andnews in messages that are up to 140 characters long. Twitter had over 500 million users
worldwide in 2012 (Semiocast, 2012) and has gained a lot of media coverage as an efficientand rapid tool for sharing emergency information (Ash, 2011). The service has also been
researched from a wide range of disciplines and research goals from political elections (Hongand Nadler, 2012), electronic word of mouth (Jansen et al. 2009), and natural disasters (Earleet al., 2011), to protest movements (Harlow and Johnson, 2011) and health information
sharing (Scanfeld et al., 2010). Some earlier research has investigated how researchers areusing Twitter at conferences (e.g., Ross et al., 2010; Letierce et al., 2010; Weller &Puschmann, 2011; Weller, Kröge, & Puschmann, 2011) but, to the best of our knowledge,
scholarly communication in general, rather than for specific purposes, on Twitter has not been
researched before, with the partial exception of a small-scale study of tweets with links from
28 scholars (Priem & Costello, 2010). To fill this gap, the current study investigates how
researchers in five diverse disciplines use Twitter. The results can both help researchers tounderstand how others are using Twitter, and hence how they may use it, and also help
scientometricians to decide if and how Twitter can be used as a scientometric data source.
Literature review
Since Twitter is relatively new, this review covers general aspects of its use as well as its
scholarly context.
General use of Twitter
Twitter has three special features that aid communication. Forwarded tweets are called
retweets and are usually marked by RT or MT for modified tweet. A second feature is the useof @ followed by a username. This can be used to send a message to another Twitter user orusers. Including @username in a tweet can also let that person know that he or she has been
mentioned. The third feature is the use of hashtags. By adding #-character followed by afreely chosen word the user can tag the tweet and hence group it together with other tweets
about the same topic. Hashtags are frequently used at scientific conferences as a convenient
way to collect all tweets about the conference together because users can set up real-timemonitoring of hashtags through Twitter to ensure that they are able to quickly access relevant
tweets. Because of the unique features of these types of tweets (RT, @username, #hashtag)
they can be extracted automatically from a corpus of tweets.
In a large scale study on Twitter Ediger et al. (2010) discovered that retweeting on Twitter has
power law-like characteristics: a few tweets are extensively retweeted whereas most tweets
are not retweeted or are only retweeted a few times. Ediger et al. (2010) found that retweets
tend to refer to a relatively small group of original tweets, which is a behavior more common
in one-to-many broadcasting rather than many-to-many communication patterns. Many-tomany broadcasting patterns were also identified in their study but in significantly smaller
subsets of the complete graph they had built from the collected tweets. This supports thebelief that we are moving away from broadcasting and broadcasted media towards networked
media and information dissemination in networks (e.g. Boyd, 2010). Twitter supports
information sharing in networks because of the social networks created by users following
other users.
Roughly 30% of all tweets have been found to be conversational in nature (Honeycutt &Herring, 2009), in the sense of using the @ convention. Huberman et al. (2008) arrived at asimilar number (25.4%) in an earlier study. Honeycutt and Herring (2009) investigated tweets
containing the @-sign and concluded that a clear majority (90%) of tweets containing the sign
were conversational. The study therefore showed that some, but perhaps not all,

conversational tweets can fairly easily be collected from Twitter, as they are usually
identifiable by the @-sign.
In their sample of 720,000 random tweets Boyd et al. (2010) found that about one third oftweets were addressing someone (using @username in the tweet), about one fifth contained a
URL, 5% contained a hashtag and only 3% were retweets. In a random sample of retweets
they discovered that over half of the retweets contained a URL and that about one fifth
contained a hashtag. The use of hashtags and URLs was therefore significantly higher in
retweets than in tweets. Suh et al. (2010) found that only about 20% of tweets contain a URL
or URLs and that almost 30% of retweets contain a URL or URLs. They also concluded that
hashtags and the type of hashtags have an impact on “retweetability”. The number offollowers also has an impact, which is quite expected. The more followers a user has the more
likely his or her tweets are to be retweeted.
People retweet for a variety of different reasons. Earlier research (Boyd et al., 2010) has
shown that people retweet because they want to spread information to new audiences or a
specific audience of followers, they may retweet because they want to comment on someone’s
tweet or make the original writer aware that they are reading their tweets. People also retweet
to publicly agree with or to validate someone’s thoughts, to be friendly, and to refer to lesspopular content in order to give it some visibility, but also for egoistic reasons such as to gain
more followers or to gain reciprocity. People also retweet to save tweets for later access.
Social media and scholarly communication
The change in scholarly communication has not been rapid because many researchers are
cautious in changing traditional scholarly communication patterns. Weller (2011, p. 55) writes
that “… research is at the core of what it means to be a scholar, and issues around quality
and reliability are essential in maintaining the status and reputation of universities. A
cautious approach is therefore not surprising as researchers seek to understand where thepotential of these new tools can enhance their practice, while simultaneously maintaining the
key characteristics of quality research”. But as more and more scholars start to use social
media it is possible that it may have an impact on tenure and promotion processes at academic
institutions (Gruzd et al., 2011).
Social media has become important for discovering and sharing research. Scholars use tools
such as wikis for collaborative authoring, tools for conferencing and instant messaging forconversations with colleagues, scheduling tools to schedule meetings and various tools toshare images and videos (Rowlands et al., 2011). Microblogging had not yet gained
significant popularity among scholars, as only 9.2% stated that they used microblogging intheir research. Rowlands et al. (2011) showed that there are some disciplinary differences in
how researchers are using social media in general, as natural scientists in their study were thebiggest users. However, they suggest that it may not take long before social scientists andhumanities researchers catch up. While there were some differences between disciplines,
differences between how different age groups use social media were not discovered.
Scholarly communication and information sharing is changing as academics increasingly use
Social Networking Sites (SNSs) such as Facebook and Twitter for professional purposes.
SNSs may promote information sharing (Forkosh-Baruch & Hershkovitz, 2011) in bothformal and informal ways. It has been shown that scholars use Twitter to cite to scientificarticles and hence Twitter could potentially be used to measure scholarly impact (Priem &Costello, 2010). Weller and Puschmann (2011) and Weller, Kröge and Puschmann (2011)
considered all tweets containing one or more URLs as a form of citation, while Priem andCostello (2010) considered a tweet as a citation only if it included a URL directly to ascientific article or to an intermediary web page that has a link to a scientific article. In adataset collected from 28 researchers’ tweets Priem and Costello (2010) found that 6% of thetweets including a URL were links to peer-reviewed articles or to web pages that link to peerreviewed articles. However, sharing links and citations are not the only scholarly activity on
Twitter. At scientific conferences for instance, Twitter is often used as a backchannel to sharenotes and resources, and for discussions about topics at the conference (e.g. Ross et al., 2010;
Letierce et al., 2010; Weller & Puschmann, 2011; Weller, Kröge, & Puschmann, 2011).
Research Questions
The goal of the research is exploratory and descriptive, driven by the following basic researchquestions.
1. What do researchers typically tweet about?
2. Are there disciplinary differences in the types of tweet sent by researchers?
The approach used to answer these questions was to gather a large corpus of tweets sent byselected researchers in five different disciplines and then to apply a content analysis to arandom sample of tweets to identify the types of content posted.
Methods
The main purpose of this research is to investigate disciplinary differences in the use of
Twitter in scholarly communication and sharing of scientific information. The five disciplines
chosen for this are astrophysics, biochemistry, digital humanities, economics, and history ofscience. These were chosen to represent variations in the traditional publishing and scholarly
communication patterns and to represent disciplines of varying size and focus.
The differences were investigated by collecting tweets sent by researchers from each of thedisciplines. First, the most productive researchers based on the number of publications fromeach discipline were queried from the ISI Web of Knowledge (WoK) database. We chose to
search for most productive rather than most cited researchers in order to find seasoned,
established researchers that already have had a long career, not just the most influential orprestigious (assuming that citations can indicate this). This was achieved through a topicalsearch for each discipline. Then a list of the most productive authors based on a count of WoS
records was extracted. Next we checked which of the top authors were active on Twitter. We
visited the homepages of the authors and searched for them on Twitter. However, this was avery time consuming method and in the end it was not possible to find many top researchers
using Twitter in this way; hence Twitter’s search function and discipline relevant keywords(e.g. astrophysics, biochemistry, etc.) were used to find other relevant researchers from theselected disciplines. The selection criterion was that the person should be active on Twitterand clearly be an established researcher in one of the chosen fields. This meant that only
tenure tracked researchers were chosen and for instance PhD students were excluded from thesample. This information was obtained from the persons’ profiles on Twitter and in cases
where this was not mentioned in the profile the user was not included to the sample. Then asnowball sampling method was used, which proved to be a good method to collect tweeting
researchers as many researchers on Twitter follow other researchers in their own field. In theend we found 45 researchers in astrophysics, 45 in biochemistry, 51 in digital humanities, 45
in economics, and 42 in history of science. The 20 most productive researchers from WoK
included only 1 Twitter user in astrophysics, 2 in biochemistry, 6 in digital humanities, nonein economics, and 1 in history of science. Hence the results do not reflect top researchers in
the disciplines but established Twitter using researchers instead.
The tweets were collected between 4 March 2012 and 16 October 2012, although some earlier
tweets will be included from the first queries. Twitter was queried at least daily for updates by

the selected users by a program accessing the main Twitter API. A few days were dropped
due to system malfunctions but since the queries could retrieve tweets from the missing
period it seems unlikely that any tweets were lost and so the collection should be quite
comprehensive. However, Twitter restricts the collection of tweets sent by certain users toapproximately 3,200 tweets. This means that for users that are not very active on Twitter we
can collect all their tweets, while from active users we only get about the 3,200 latest tweets.
Within the time period of data collection a total of 59,742 astrophysics tweets, 40,128
biochemistry tweets, 89,106 digital humanities tweets, 57,673 economics tweets, and 58,414
history of science tweets were sent by the researchers. There were disciplinary differences in
the amount of tweeting: in astrophysics the researchers posted on average 1328 tweets each,in biochemistry 892 tweets per researcher, in digital humanities 1747 tweets per researcher, ineconomics 1282 tweets per researcher, and in history of science 1391 tweets per researcher.
This shows that biochemists were least active Twitter users, while digital humanities
researchers were the most active.
From each discipline 200 tweets were randomly selected using a random number generator
for a faceted content analysis. The 200 tweets from each of the disciplines were grouped into
four categories for facet 1: Retweets, Conversations, Links, and Other. The category Retweets
included tweets that were identified by RT or MT (modified tweets), or tweets that wereotherwise marked as having been sent via someone else. The Conversations categorycontained tweets that were not retweets and that were identified by @username, indicatingthat the tweet was sent to someone. The categories do not therefore include any conversations
that have been held without using the @username convention, but as earlier research suggests(Honeycutt & Herring, 2009), it should be possible to collect most of the conversational
tweets with this method. The Links category contained tweets that were not retweets orconversations and contained a URL (usually shortened). The Other category contained all theremaining tweets.
For facet 2, the tweets were categorized according to scientific and disciplinary content. These
categories were: Scholarly communication, Discipline-relevant, Not clear, and Not about
science (Table 1). The first category contained tweets that clearly were about science andclearly on topic about the chosen discipline. Tweets in the second category were clearly about
the discipline but not clearly about science in the sense of conducting or discussing scientificresearch. In the third category it was not clear if the tweets were about science or if they even
were about the discipline. Tweets in the final category were clearly not about science nor
were they about the discipline in question. A conservative approach was used whenclassifying the tweets. This means that when in doubt a less scientific category was chosen inorder to prevent overestimation of the scientific content in the analyzed tweets. Also, every
tweet was classified into only one category. The whole sample was coded by the first author
and a random set of 25% (50 tweets) of the tweets from each discipline were coded by anotherresearcher to check for inter-coder reliability. After the first round of coding the researchers
talked through the cases where they did not agree and refined the coding scheme based on thediscussion. Then a second round of coding was conducted with a new random set of 25% of
the tweets and the standard Cohen’s Kappa statistic was used to assess the reliability of theclassification in this second round.
Table 1. Categorizing tweets according to scientific and disciplinary content

Category
Description
Scholarly
Tweets that are clearly scientific and oncommunication topic of the discipline. This includes tweets
with links to scientific papers or journals,
sharing research results, comments,

Example of tweet“Decellularized matrix fromtumorigenic humanmesenchymal stem cells
promotes

Disciplinerelevant

Not clear

Not aboutsciencequestions and answers of a scientific nature. neovascularization...
Tweets in this category clearly have some http://t.co/aF6TVFIG”
scientific value for other researchers.
(link to an abstract in
PubMed)Tweets that are clearly on topic of the “Fri AM in Asia: Asian
discipline but are not clearly scientific as stocks already heading
described in the category above.
downward. 50-50 chance of
global recession.”
Both scientific and disciplinary relevance “@[…] Your welcome :)”are not clear. Usually because there is notenough information in the tweet for otherjudgements. The tweets in this category
could be fractions of conversations or shortanswers to earlier questions from anotherperson.
Tweets that are clearly not scientific nor on “The goddamn mice havethe topic of the discipline. This includes been at the wiring of my car
personal tweets, links to photos, comments again. As a bonus theabout everyday life in general, and status dealership wi-fi blocks twitter
updates about what they were doing and and they have no power
where they were at the moment.
outlets.”

A chi-square test was used to assess whether the disciplines had overall different proportionsof tweets falling in each category. Differences in proportions tests at the fixed level p=0.05
were used to test for differences between disciplines for individual categories. These tests
were indicative rather than statistically rigorous, however, because we did not have a prior setof hypotheses to test for and so we could not conduct a small enough number of specific tests
to control for errors with a Bonferroni correction other than one that compensated for all
possible tests.
Results
There were some disciplinary differences in the types of tweets that were sent (Figure 1),
confirmed by a chi-square test (p=0.000). In biochemistry 42% of the tweets were retweets incomparison to about 25% in the other disciplines (sig. p <0.05). Conversations were importantin astrophysics (31.5% of the tweets), digital humanities (38%) and history of science(28.5%). The proportions of conversations in biochemistry and economics were much lower
in both cases at about 16% (difference between the two sets sig. p <0.05). Conversations ingeneral were roughly twice as important in astrophysics, digital humanities and history ofscience compared to biochemistry and economics. When collecting random tweets only one
part of a conversation is available, which makes it difficult to judge whether conversations areabout science or not. An example of an unclear tweet is “@[…] Yup! I will indeed keep you
posted.” It is possible that the conversation is about science, but it could be about somethingelse too. Economics shared most (sig. p <0.05) links (38%), but sharing links was importantalso in the other disciplines. In history of science 27% of the tweets were shared links, inastrophysics the amount was 23.5% and in biochemistry 21.5%, but in digital humanities only
15.5% of the tweets were links (sig. lower than all the others except biochemistry, p <0.05).
Of course some of the retweets and conversations also contained links, however the purposeof sharing the links in these categories can be assumed to be somewhat different than in
tweets that are neither forwarded information (retweets) nor part of conversations between
two or more persons. When classifying the tweets according to type the inter-coder agreement

was very high; only in two cases out of the 250 tweets that two researchers coded had theresearchers coded the tweets differently.
A considerable proportion of the retweets contained links. About 75% of retweets inastrophysics contained one or more links, in history of science 70%, in biochemistry about
68%, in economics about 65% and in digital humanities about 62% of retweets contained
links. This clearly shows that researchers in these disciplines frequently share web content
and forward information and content they have received from people they follow on Twitter.
The remaining tweets made up between one fifth to one fourth of the total tweets in eachdiscipline (Other category).
100 %
90 %

21.5

20

24.5

21.5

15.5

19.5

21.5

80 %
70 %

23.5

60 %

38

50 %
40 %

Other

16.5

Links
38

31.5

28.5
16

30 %

Conversations
Retweets

42

20 %
10 %

27

23.5

22

24.5

25

Digitalhumanities

Economics

History ofscience0%Astrophysics

Biochemistry

Figure 1. Types of tweets by discipline

There are clear disciplinary differences in the amount of tweets in the scholarly
communication category (Figure 2), confirmed by a chi-square test (p=0.000). Almost 34% of
the tweets in biochemistry were clearly part of scholarly communication (sig. greater than theothers, p <0.05), and in astrophysics the number is 23% and in digital humanities 22%. In
history of science and economics the number is substantially lower than the others (sig. p<0.05), at 7.5% and 6.5% respectively.
Few economics tweets were clearly for scholarly communication, but many tweets were about
economics in general. Some of these may be scholarly communication but it is not clear based
just on the tweet. An example of an unclear tweet is the following: “RT @HarvardBiz Africa's Growth Opportunity - Swaady Martin-Leke and Loic Sadoulet - Harvard Business
Review: http://t.co/5WAv7qCJ”. The link is to a blog entry in Harvard Business Review from
October 2011. The tweet is clearly about economics, but whether the blog entry has scientificvalue for a researcher is unclear. Economics is a general topic of discussion for citizens and so
academics discussing economic issues are not necessarily discussing research, and hence it isdifficult to judge whether tweets are about economics or research in economics. Economics
had the most tweets that were discipline-relevant (51.5%, sig. p <0.05). The other disciplines
had between 22% and 8.5% tweets that were discipline-relevant. While the other disciplines
had between 26% and 34% tweets that were not about science nor about the discipline, in
history of science 57.5% of tweets were clearly not about science nor about history of science(sig. greater than the others p <0.05). History of science stands out of the group as only 16%of the tweets were for scholarly communication or discipline-relevant, while the same forother disciplines was substantially higher.
One quarter of the tweets from the random sample were coded twice by two researchers. Afterthe second round of coding the researchers coded the tweets to the same categories in 68.9%of the cases. The standard Cohen’s Kappa statistic gave an inter-coder reliability of 0.587,
which constitutes as “good” or “moderate” agreement, depending on which interpretation one
uses (Fleiss, 1981; Landis & Koch, 1977).
100 %
90 %
80 %

29.5

29

34

70 %
60 %

16
25.5

Clearly not science
31.5

40 %

Not clear

13.5

Discipline relevant

22
12.5

20 %
10 %

57.5

24

50 %

30 %

26

23

51.5

33.5
22
6.5

0%
Astrophysics Biochemistry

Digitalhumanities

Economics

26.5

Scholarly communication

8.5
7.5
History ofscience

Figure 2. Relevance of tweets by discipline

All disciplines had retweets for scholarly communication (Figure 3), but especially inbiochemistry retweets (18% of all tweets in the discipline) appear to be an important tool toforward scientific information. In economics and history of science the importance of retweets
was marginal for scholarly communication. In all disciplines less than 3.5% of theconversations were clearly part of scholarly communication. In fact, none of the conversationsin economics and only one conversational tweet in history of science were clearly part ofscholarly communication. Both in astrophysics (10%) and in biochemistry (7%) researchers
share links to scientific content, while somewhat less scientific links were shared in the otherdisciplines. Some evidence of scholarly communication was also found in the remaining
tweets in the Other category.

40%
35%
30%

5

25%

7

20%

3.5

15%

10

10%
5%

3.5

18

3

Other

0%
Astrophysics

Biochemistry

Conversations

3
3
8.5

6.5

Links

7.5

Digitalhumanities

Retweets
0.5
501
Economics

1.5
4.5
0.5
1
History ofscience

Figure 3. Percentages of scholarly communication tweets by type

An informal content analysis of the tweets from the Scholarly communication category
showed that the retweets are mainly links to articles in popular science magazines, to blog
entries, to newspaper articles, and to promote upcoming events, articles, interviews and radio
shows. While almost all of the relevant retweets included links, only four of the retweets
contained a link to a scientific paper or to an abstract. In Conversations it was not usual toshare links, but rather to share opinions, talk science or comment on science facts withcolleagues. There were only two tweets with links to scientific papers; one to a publisher’s
abstract page with a link to full text, and one directly to a pdf file.
In the Links category tweets included links to articles in popular science magazines and to
blog entries. In addition, 16 tweets contained a link to a scientific paper. Of these four werelinks directly to the full text files, 5 were to the publishers’ page, and 3 were to other online
texts that had links to the publishers’ page for the article. Of these 16 links to scientific papers
8 were in astrophysics, 4 in biochemistry, 2 in economics, and 1 each in digital humanities
and history of science. The remaining links were to an editorial in a scientific journal, a draftof a scientific paper, an abstract in an online database, and to the literature list of an online
article. In the Other category the tweets were mainly comments and opinions on science facts,
promotional or about some workshops or conferences. None of the tweets in this categorycontained links to scientific articles. A total of 22 links were to scientific papers. This
constitutes 2.2% of all tweets, which is somewhat lower than the 6% found by Priem andHemminger (2010) in their sample.
Discussion and conclusions
In answer to the second research question, the results suggest that there are clear differencesin Twitter use between disciplines. Researchers in every discipline retweet, but they do soalmost twice as much in biochemistry than in the other disciplines. Researchers forward
information substantially more than the average Twitter user does. Boyd et al. (2010) foundthat only about 3% of tweets were retweets, while in our research we found that on average
27% of the tweets across the five disciplines were retweets. In digital humanities researchers
use Twitter more for conversations than in the other disciplines, and substantially more thanin biochemistry and economics. In economics Twitter is used mostly to share links, while thispossibility did not seem to be frequently used in digital humanities.

Based on the results it also seems clear that Twitter is used more for scholarly communicationin biochemistry and astrophysics (and to some extent in digital humanities) than in economics
and history of science. Least evidence of scholarly communication was found among thehistory of science researchers. Economics proved to be a difficult discipline to evaluate
because economics is a common topic of discussions among citizens and because of that
researchers discussing economics or sharing news and information about economics, do notnecessarily mean that they are involved in scholarly communication.
It seems clear that researchers share more links than the average Twitter users. Both Boyd etal. (2010) and Suh et al. (2010) found that about 20% of tweets contained links, while in our
research we discovered that on average 25% of the tweets contained links, and this is
excluding the retweets, of which most contained links. The difference between researchers’
use of Twitter and the average Twitter user is in particularly clear in the retweets where
between 62% and 75% of the researchers forwarded tweets including links to some
information resources. In many cases the information shared was related to the discipline, but
not necessary to scientific publications. The multitude of different types of information andcontent shared also shows how researchers are using an abundance of different information
sources when keeping themselves up-to-date with news and events in their discipline. How
many of these directly benefit their research work is not clear and more qualitative research isneeded to fully understand how and why researchers are using social media sites such as
Twitter in scholarly communication. In fact, a possible future research direction could be aqualitative investigation about how the researchers themselves in specific disciplines believethat they are using Twitter (and whether that is in correlation with the results discovered in thepresent study or not) and what kind of possible scholarly benefits they have identified withthe microblogging site (for a single discipline, see Priem & Costello, 2010).
Although the biochemistry researchers were the least active Twitter users they were the groupthat used Twitter most for scholarly communication. Researchers in digital humanities on theother hand used Twitter most actively, but mainly for conversations that were not clearlyscientific. Moreover, 57.5% of the tweets by researchers in history of science had nothing to
do with science or history of science. These were mainly comments about their everyday lives
or status updates about where they were and what they were doing. When analyzing thescholarly communication tweets, few cited research articles directly or indirectly. Only 2.2%of all tweets were like citations in the sense of linking to an academic article. The results
suggest that Twitter is for many researchers an important tool in scholarly communication,
but it is not frequently used to share information about scientific publications. The results alsosuggest that disciplinary differences in the use of Twitter are a fact that has to be taken into
account in any future research about scholarly use of Twitter.
Some evidence was discovered that researchers use Twitter to share information about, andlinks to, scientific articles. However, these were only discovered after the links were manually
visited, a procedure that is not reasonable to replicate with a large dataset and for which there
are currently no automated procedures for. It is possible to collect all tweets containing
specific URLs or top-level domains of links to some publishers article collections, forinstance
http://www.plosone.org/article/info:doi/
(to
PLOS
One)
or
http://www.emeraldinsight.com/journals.htm?issn=0022-0418(totheJournalof
Documentation), but it would not be possible to cover all publishers, online open accessjournals, institutional repositories and URLs to self-archived papers.
The present research has a number of weaknesses, of which the most significant is in thecoding of the tweets. While categorizing the tweets according to type is fairly straightforward, classifying by relevance for scholarly communication is more difficult. Although the
Cohen’s Kappa value for inter-coder agreement was 0.587 in this research, it is possible that
other researchers with background in some of the disciplines in this research might come to adifferent conclusion regarding the scientific value of some tweets. However, even thesetweets should be covered in the first two categories of this research, scholarly communicationand disciplinary-relevant, and hence they would have been included as relevant tweets evennow. Also, to prevent overestimation of the results we used a conservative approach in thecoding, meaning that when in doubt the tweets were coded into a less scientific category. In
addition, other fields may have given different results and so, even when the results agree forthe five covered here, they cannot be confidently generalized.
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 UnitedStates (Grant No. 1208804), JISC in the United Kingdom, and the Social Sciences andHumanities Research Council of Canada. The authors wish to thank Andrew Tsou for his help
in coding the tweets and the anonymous reviewers for their comments.
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