Content uploaded by Stefanie Haustein
Author content
All content in this area was uploaded by Stefanie Haustein on Sep 01, 2014
Content may be subject to copyright.
Astrophysicists’ Conversational Connections on Twitter
Kim Holmberg
1,2
*, Timothy D. Bowman
3,4
, Stefanie Haustein
4
, Isabella Peters
5
1School of Mathematics and Computing, University of Wolverhampton, Wolverhampton, United Kingdom, 2A
˚bo Akademi University, Turku, Finland, 3Dept. of
Information and Library Science, Indiana University, Bloomington, Indiana, United States of America, 4E
´cole de bibliothe
´conomie et des sciences de l’information,
Universite
´de Montre
´al, Montreal, Canada, 5ZBW Leibniz Information Center for Economics and Christian Albrechts University Kiel, Kiel, Germany
Abstract
Because Twitter and other social media are increasingly used for analyses based on altmetrics, this research sought to
understand what contexts, affordance use, and social activities influence the tweeting behavior of astrophysicists. Thus, the
presented study has been guided by three research questions that consider the influence of astrophysicists’ activities (i.e.,
publishing and tweeting frequency) and of their tweet construction and affordance use (i.e. use of hashtags, language, and
emotions) on the conversational connections they have on Twitter. We found that astrophysicists communicate with a
variety of user types (e.g. colleagues, science communicators, other researchers, and educators) and that in the ego
networks of the astrophysicists clear groups consisting of users with different professional roles can be distinguished.
Interestingly, the analysis of noun phrases and hashtags showed that when the astrophysicists address the different groups
of very different professional composition they use very similar terminology, but that they do not talk to each other (i.e.
mentioning other user names in tweets). The results also showed that in those areas of the ego networks that tweeted more
the sentiment of the tweets tended to be closer to neutral, connecting frequent tweeting with information sharing activities
rather than conversations or expressing opinions.
Citation: Holmberg K, Bowman TD, Haustein S, Peters I (2014) Astrophysicists’ Conversational Connections on Twitter. PLoS ONE 9(8): e106086. doi:10.1371/
journal.pone.0106086
Editor: Lutz Bornmann, Max Planck Society, Germany
Received May 22, 2014; Accepted July 29, 2014; Published August 25, 2014
Copyright: ß2014 Holmberg et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. The Tweet IDs have been
uploaded to figshare:http://figshare.com/articles/Tweet_IDs_from_Astrophysicists_conversational_connections_on_Twitter_/1119276. Please cite as: Holmberg,
Kim (2014): Tweet IDs from ‘‘Astrophysicists’ conversational connections on Twitter’’. figshare. http://dx.doi.org/10.6084/m9.figshare.1119276. Retrieved 09:16, Jul
28, 2014 (GMT).
Funding: This research was part of the international Digging into Data program (funded by Arts and Humanities Research Council/Economic and Social Research
Council/Joint Information Systems Committee (United Kingdom), Social Sciences and Humanities Research Council (Canada), and the National Science
Foundation (United States; grant #1208804). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* Email: kim.holmberg@abo.fi
Introduction
Astrophysics and astronomy are examples of academic disci-
plines that engage with the public and with scholars across
disciplines to identify novel objects and recurring patterns in large
data sets to help answer research questions. According to NASA
[1], citizen scientists have helped answer ‘‘serious scientific
questions’’ and provided the astronomical community with ‘‘vital
data.’’ Christian, Lintott, Smith, Fortson, and Bamford [2]
describe citizen scientists as being actively involved in achieving
real research objectives. Projects related to astronomy and
astrophysics like Galaxy Zoo (http://www.galaxyzoo.org), with
more than 250,000 volunteers classifying galaxies (http://authors.
galaxyzoo.org), and the Milky Way Project (http://www.
milkywayproject.org), highlight the communications between
researchers in astrophysics as well as astronomy and a broader
audience that can include non-experts, volunteers, and collabora-
tors from outside disciplines. As Kouper [3] notes, there is a
‘‘range of new ways of engaging the public in dialog and decision
making… (that) have been introduced in practice and scholarly
literature.’’ One way for scholars to engage both other scholars
and groups participating in citizen science at large is through the
use of social media applications.
An example of a social media application that has been shown
to be of use to scholarly communication and citizen science is the
blog. Bloggers use the medium to communicate their feelings,
thoughts and reaction to matters of interest [4]. Scholars have
been found to use blogs in order to ‘‘provide authoritative opinions
about pressing issues in science… (and) because of their
freewheeling nature, these blogs take scientific communication to
a different level’’ [5]. In other scholarly communication discourse,
research has shown that scientists who have blogs tend to discuss
recent publications, socially relevant information, high-quality
science, and that they write in a manner in which the information
is useful to both academics and non-academics [6]. Niset [7]
argued that ‘‘scientists… must strategically ‘frame’ their commu-
nications in a manner that connect with diverse audiences’’ and
that scholars should no longer assume that simply bringing the
public updated information about scientific facts is enough;
instead, scholars should engage the public’s ‘‘values, interests,
and worldviews.’’ The contributions of the contemporary scholar
can be found in blogs, but also in other social media. Another
context in which scholarly communication is occurring is in the
microblogging site Twitter, where communication and interaction
with a general audience are possible. The number of Twitter users
grew by 39% from September 2012 to September 2013 (http://
www.sec.gov/Archives/edgar/data/1418091/
PLOS ONE | www.plosone.org 1 August 2014 | Volume 9 | Issue 8 | e106086
000119312513400028/d564001ds1a.htm), currently reaching 230
million active users, who post 500 million tweets per day (https://
business.twitter.com/whos-twitter). It is the scholarly activity
within this medium that is of interest to this work.
It has been suggested that traces of scholarly activities and
conversations left in social media might convey something about
the impact or visibility of scholarly research. Altmetrics is the
research area that investigates these possibilities. While altmetrics
still lacks a widely agreed definition, the concept is typically used to
describe the measurement of impact or visibility of scientific
articles and other scholarly activities in social media such as blogs,
tweets, Facebook ‘likes’ and social bookmarks [8]. While scholars
are using many different social media sites for scholarly
communication, Twitter seems to be one of the most promising
contexts in which to perform altmetric research because it contains
more scientific content than many other social media sites [9].
Twitter is also the second largest source – behind the social
reference manager Mendeley – of altmetric data that can be
currently collected [9,10]. Many papers [11,12,13] discuss
Twitter’s usefulness for scholarly communication, particularly in
terms of distributing information to a wider audience of
researchers and the general public. Data can be collected via
Twitter’s API and filtered in great detail by taking advantage of
the API documentation and metadata that is included with the
data. However, the content of the activities and the context in
which these traces of research activities are created on Twitter are
still relatively uninvestigated areas, even though they impact the
validity of altmetrics. This raises several questions about altmetrics
and the value it provides to the scientific community, including:
Are the traces created in tweets conversations among scholars or
do they represent activities where science is communicated to a
wider audience? Does the content of these interactions reflect
research activities of the tweeters? Does tweeting activity or
publishing activity of the researchers have an impact on how and
for what purpose they use Twitter? With whom do researchers
interact with on Twitter?
To increase our collective understanding of the context in which
potential altmetrics indicators are created on Twitter we need
more research about how social media is used and perceived by
researchers. In order to better understand the impact and context
of scholarly communication on Twitter, this paper maps the
conversational connections of a group of astrophysicists using
Twitter and analyzes the content of their tweets. Active
engagement with the public (i.e. citizen scientists) and active use
of social media (see http://www.wired.com/2013/11/nasa-
socials) makes astrophysicists an interesting group to study Twitter
use for scholarly communication. Because of this, the results may
not be generalizable to other disciplines, but rather present a
particular case. In this paper we seek to answer the following three
research questions:
1. With whom do astrophysicists have conversational connections
on Twitter? Who do they mention in their tweets?
2. Does traditional scholarly communication (i.e., publication
frequency) affect conversational behavior of astrophysicists (i.e.,
tweeting frequency, with whom they talk)?
3. Do tweets of astrophysicists show syntactical and linguistic
particularities, like intensive use of hashtags or emotions?
Our hypothesis is that the contexts in which attention and
visibility are created in Twitter and the intended audience of that
content have impact on whether altmetrics can be used to evaluate
influence or visibility of scholarly communication. By mapping
who the astrophysicists mention in their tweets we will learn about
the intended audience of astrophysicists Twitter communication
and by answering research questions 2 and 3 we will learn more
about the context in which the tweets have been published.
Literature Review
It has been reported that social media, like blogs and Twitter,
are used in academia ‘‘at all points of the research lifecycle, from
identifying research opportunities to disseminating findings’’ [14].
Scholars have reported both being familiar with a diverse set of
social media tools and that they would like to increase their usage
of these tools in the future [15]. However, different surveys have
reported very different usage figures. Rowlands et al. [14] reported
that around 80% of researchers used social media in research,
while 66% of the 939 professors in the study by Moran, Seaman
and Tinti-Kane [16] reported using social media in the past
month. In these studies social media was defined rather broadly to
include tools for conferencing and collaborative authoring, which
may explain the high percentage of usage.
Twitter uptake and use
Recently Twitter has become one of the most popular social
media sites [17], but while the coverage of scientific content is
higher on Twitter than on many other social media sites [9], the
actual use of Twitter in scholarly communication still remains low.
According to Rowlands et al. [14] and Priem, Costello and Dzuba
[18] respectively, 9.2% and 2.5% of scientists are active on
Twitter. Haustein et al. [19] found that among 71 surveyed
participants at a bibliometric conference in 2012, 43.7% had a
Twitter account. These were mostly used for private reasons, to
connect with people professionally, to distribute professional
information and to improve one’s visibility on the web. Bowman
et al. [20], who surveyed over 200 Digital Humanities scholars,
found that 80% of respondents rated Twitter as a relevant source
of information for digital humanities research and 73% rated
Twitter as relevant for dissemination of research information. In a
recent survey Pscheida et al. [21] asked a representative sample of
scholars at German universities about social media use and found
that 15.2% of 778 (cases were weighted according to type of
university and region to account for particular over- and
underrepresentation in the sample.) participants used Twitter.
Of those 118 (weighted) who reported using Twitter, 30.5%
reported using it only for private communications while 69.5%
used it occasionally in a professional context. In addition, 22.0% of
respondents reported using Twitter daily and 73.2% used it at least
once per week. Because Twitter was known by 97.2% of the
German survey respondents, Pscheida et al. [21] conclude that the
microblogging platform is rather a hype medium that is mostly
spoken about but rarely used in scholarly communication (at least
in Germany). The variation from 2.5% to 80% in Twitter uptake
among scholars is influenced by both the differences between
scientific disciplines [13] and the time when the survey was
conducted. However, both surveys including all fields of science
suggest that work-related Twitter use among scholars remains low
at around 10%.
Twitter affordances
Although Twitter is not designed as a social network where
users are connected via mutual relationships, user networks and
conversations do evolve through Twitter-specific affordances like
following another Twitter user, retweeting someone’s tweets,
mentioning usernames in the tweets and by using the same
hashtags. Users can subscribe to another user’s Twitter time line
by following his or her account (i.e. forming a directed relationship
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 2 August 2014 | Volume 9 | Issue 8 | e106086
between the follower and the followee). Another distinctive
affordance available to users is the retweet. A retweet occurs
when a user redistributes another’s tweet. Because of the specific
format of this affordance, one can detect so-called pure retweets
(e.g. tweets that start with ‘RT’) in order to analyze how
information is disseminated and forwarded through the Twitter
networks. In addition to the retweet, Twitter also affords users the
ability to direct tweets at other users through the use of an ‘@’
symbol followed by a username. Honeycutt and Herring [22] have
shown that the use of the mention affordance is a strong indication
that the tweet is conversational in nature as about 90% of the
tweets containing @username were found to address a user as part
of a conversation. Honeycutt and Herring [22] further discovered
that about one third of all tweets in their study contained a
username, thus being conversational. In a way, Twitter has
become the digital water cooler around which users discuss their
work [23]. In addition to retweets and mentions, users also make
use of the hashtag affordance to categorize, organize, and retrieve
tweets. The use of hashtags is extremely popular during major
events (e.g., televised events such as the #royalwedding), natural
disasters (e.g., #tsunami [24]), or scientific conferences (e.g.,
#asist13 [25]). As such, hashtags may resemble the traditional
function of metadata by enhancing the description and retriev-
ability of documents.
Sentiment of Tweets
The linguistic construction of tweets, especially the use of
emotional-laden terms, may also affect conversations and a tweet’s
dissemination. Tweets containing strong sentiments are found to
be retweeted more often than neutral tweets [26,27], which leads
to the assumption that emotional tweets are more likely to be
widely distributed. The level of activity or experience, in terms of
the number of tweets posted by a Twitter user, has not been found
to influence the sentiment of the tweets (i.e. sentiment of tweets
from more active users do not differ from the sentiment of tweets
from anyone else [26]). It has, however, been shown that adding
positive emoticons to tweets is very common and that, at least in
one case, 85% of a particular set of tweets had positive sentiments
[28,29]. However, Thelwall [30] came to a contradictory
conclusion by discovering that sentiments are barely expressed
in tweets finding that the sentiment of tweets did not change even
when the covered event turned out to be very negative. Hence,
Thelwall [30] concluded that sentiment analysis is not able to
properly detect linguistic phenomena like sarcasm and irony from
messages of limited lengths like tweets.
Given that we consider tweets as medium for scholarly
communication we have to look at work discussing the expression
of sentiment in scientific publications. Verlic, Stiglic, Kocbek and
Kokol [31] analyzed frequently used strong adjectives and adverbs
in a five-year span of conference papers to detect enthusiastically
and passionately presented research results. They concluded that:
‘‘we could not claim that sentiment as defined in scope of our
study is obviously present in the papers we analyzed.’’ Small [32]
published an exploratory study on how attitudes towards cited
work were expressed in co-citation networks finding that
sentiments were not understood as positive or negative emotions
but as structural terms for argumentation (e.g., discovered,
demonstrated) or description of scientific results (e.g., approaches,
fundamental). He showed that sentiments vary in citation contexts
of different disciplines and ‘‘provide insights into the current issues
and concerns of a research community’’ (p. 387). There have been
a few others [33,34] who have looked at this phenomenon, but
overall the research in that area is sparse. Because the research is
sparse, Twitter continues to be a promising context to study given
the findings of former analyses of sentiments and distribution
patterns on Twitter [35].
Twitter as an altmetric source
Earlier studies examining the impact or visibility of research
using traces of scientific activities in social media have discovered a
correlation between altmetric indicators and more traditional
measures of scientific impact such as citation counts [36,37],
although more recent findings have questioned this correlation. A
large-scale study [10,38] based on 1.4 million PubMed papers
found that correlations are generally very low and vary by
scholarly sub-discipline as reflected in Spearman values of citations
and tweets ranging between 20.200 (Speech-Language Pathology
& Audiology) and .327 (General & Internal Medicine). It has also
been shown that results may be significantly impacted by the time
of tweeting and time of article publication as ‘‘comparisons
between citations and metric values for articles published at
different times, even within the same year, can remove or reverse
this association’’ [9]. Another issue worth mentioning here
concerns the different versions of the same publication or the
same research that may exist (e.g., preprint, conference proceeding
and journal article) and that all can receive altmetrics. Should all
of these altmetrics be aggregated or should they be treated
separately? Haustein and colleagues [39] combine tweets men-
tioning the arXiv e-print and the paper published in the journal of
record handling the as two versions of the same document, but it
could very well be argued that various versions represent different
publications, particularly if significant changes were made during
the review process.
Haustein, et al. [40] discovered that, for a set of astrophysicists
on Twitter, those that were more active on Twitter (i.e. published
more tweets) published less scientific articles and vice versa; this
negative correlation between tweeting activity and publishing
activity may have significant impact on the reliability and
generalizability of altmetric measures. Those researchers that do
not actively participate in social media to promote and discuss
their own work may be left in a disadvantaged position compared
to those researchers that actively engage in online communication.
This also raises the question of gaming the altmetrics; when is one
only promoting his or her own work and when is it considered as
gaming the numbers to intentionally inflate one’s online visibility?
It is also possible that if the altmetrics are created by a certain type
of users (those that are active in social media) it may undermine
the generalizability of the altmetrics. It is clear that more research
is needed to investigate the content and context in which scholars
use Twitter and what role it plays in scholarly communication.
This work addresses this need by examining tweets and Twitter
conversations of a sample of astrophysicists.
Methods and Data Presentation
A total of 68,232 tweets published by 37 astrophysicists were
retrieved through the Twitter API in May 2013. The 37
astrophysicists represent a wide selection of astrophysicists, with
great variation in both publishing and tweeting activity. They also
represent different levels of academic seniority. A more detailed
description of the sample of astrophysicists and tweets can be
found in Holmberg and Thelwall [13] and Haustein et al. [40].
The 37 astrophysicists mentioned a total of 11,252 unique
usernames in their tweets and each username was mentioned on
average 10 times, while the median for the whole dataset was 1;
this indicates highly skewed data (Figure 1).
In order to investigate who the astrophysicists approach or
mention in their tweets we created an ego network map of the
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 3 August 2014 | Volume 9 | Issue 8 | e106086
tweet authors and the usernames they mentioned. As we neither
know whether these tweets were part of a dialogue or conversa-
tions between the astrophysicists and the other users, nor is it the
purpose of this paper to investigate the full communication
network of the astrophysicists, we will call these connections
between the astrophysicists and the usernames they mention
‘conversational connections’. A conversational connection there-
fore indicates that a conversation has been initiated or that a
certain username has been approached or at least mentioned by
one of the 37 astrophysicists investigated in this research. The 37
astrophysicists mentioned a total of 11,252 usernames in their
tweets, creating 56,415 conversational connections between the
astrophysicists and the other users mentioned in their tweets. If
tweets by the astrophysicists mentioned more than one username,
then all usernames were extracted and treated as co-mentions of
source-target pairs (i.e., astrophysicist1– username_1; astrophysi-
cist1– username_2, …, astrophysicists1– username_n). Webo-
metric Analyst software [41] was used to create a conversational
network based on these conversational connections and Gephi
software [42] was used for network visualization.
The network was limited to users that were mentioned or that
had tweeted 20 or more times. For the final analysis we included
32 astrophysicists (tweet authors) as well as 511 usernames that
were mentioned in the tweets. Because the 32 astrophysicists were
in contact with 511 people and the groups overlapped, 518 of the
most mentioned usernames are represented in the conversational
network. The underlying matrix thus contained 32 rows and 518
columns, where the cells contained the number of conversational
connections with a minimum of 20 occurrences. By using a small
set of astrophysicists as a seed set, we also made sure it was possible
to manually code users in order to find out with whom these
astrophysicists actually communicate on Twitter. The usernames
in the conversational network were coded according to their role
or professional titles as 32 astrophysicists (the seed dataset),
amateur astronomers,corporative,organization or association,other
astrophysicists,other researchers,science communicators,students,
teachers or educators,other or unknown based on information
found directly in their Twitter profile or by following provided
links. For instance, the science communicators category included
science bloggers and science journalists, while the organizations
and associations category included organizations related to
astrophysics or astronomy (e.g. NASA, ESA and ESO). Others
included users that could not be coded into the other categories
and unknown included those users whose role or profession could
not be determined due to lack of information in their Twitter
profiles or links. The categories were created inductively, thus new
categories were created when users did not fit into existing
categories. The coding was carried out by one of the authors.
Gephi’s community detection [43,44] was used to detect more
densely clustered groups of users based on the number of
connections between them. The content of the conversations
and the professional makeup of these clusters were analyzed in
order to learn more about the conversational connections of the
astrophysicists.
In addition to the conversational connections, we also analyzed
the content of the tweets and the use of hashtags to determine both
popular hashtags used by astrophysicists and whether hashtag
sharing among astrophysicists leads to the development of online
communities. We defined a hashtag as any string of characters
between a ‘#’ symbol and a blank space (e.g., #NASA) and
automatically extracted all such occurrences from the tweets.
Poorly constructed hashtags (e.g., #(blank space) term) were not
Figure 1. The frequencies with which usernames were mentioned on a log-scale. Figure 1 shows how skewed the frequency with which
usernames were mentioned was, with a few usernames that were mentioned frequently and with a lot of usernames that were only mentioned once
or just a few times.
doi:10.1371/journal.pone.0106086.g001
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 4 August 2014 | Volume 9 | Issue 8 | e106086
captured for this analysis. The hashtags were analyzed according
to the communities detected in the graph of ego networks. In order
to learn more about the content of conversations in the
conversational clusters that were detected with Gephi, we used
VOSviewer [45] to extract noun-phrases from the tweets.
VOSviewer applies a linguistic filter based on a part-of-speech
tagger, which extracts noun phrases and merges regular singular
and plural forms [45]. We included all the noun-phrases in the
analysis, thus no thresholds or relevance scores offered by
VOSviewer were applied to restrict the results. The similarities
between the noun phrases used in each cluster were measured
using Pearson’s r.
The SentiStrength tool [35] was used to determine the
sentiments of tweets in each cluster. SentiStrength uses a lexical
approach with sentiment-word-lists as well as several rules to
process linguistic variation in terms. The tool was especially
constructed for analysis of short texts found on the (social) Web
(e.g., taking into account exorbitant use of punctuation [35]). The
sentiment analysis results provide two scores for each analyzed
word (i.e. negative and positive) that ranged between 25 and 21
and 1 and 5 respectively. Score of 1 and 21 indicate that the word
is neutral and has no sentiment. The mean positive and mean
negative values as well as a combined Sentiment Score (i.e.
negative values plus positive values) for the tweets in each cluster
were measured and compared.
Results
The 518 nodes (representing the 32 astrophysicists and the
usernames they mentioned in their tweets) were connected with
each other through 2,395 edges resulting in a set of 27,923
conversations (it should be noted that if astrophysicist1 mentioned
two other people in a single tweet, that counted for two
conversational connections). Figure 2 shows the number of people
the 32 astrophysicists mentioned in their tweets and the number of
conversational connections they had with these users. The
frequency with which the astrophysicists mentioned other
usernames varied a great deal; results fell along a continuum
between over 2,500 conversational connections with almost 200
different usernames, to only a few conversations with a few users.
Overall there is a strong correlation between the number of users
mentioned and the number of conversations (0.94 with Spearman
rank correlation), but this correlation and Figure 2 also indicate
that some astrophysicists have more conversations with fewer users
while others have their conversations with a much wider audience.
For clarity the 32 (6.18%) astrophysicists whose tweets we
retrieved were simply labeled as ‘‘32 astrophysicists’’ (Table 1), the
other usernames were coded according to their role or professional
titles. Most of the users mentioned in the tweets by astrophysicists
were coded as science communicators (24.13%), other astrophys-
icists (21.62%), organizations or associations (13.32%) and others
(11.20%). A perhaps surprisingly low number of the users
mentioned were teachers, students, or amateur astronomers.
Especially with the large number of citizen scientists involved in
various projects related to astrophysics we would have expected
astrophysicists to interact with amateur astronomers on Twitter
and thus see more mentions of non-scientists.
In another work [40], the astrophysicists were categorized
according to their tweeting activity and their publishing activity.
We used these categories to analyze whether tweeting activity or
publishing activity had an impact on conversational connections in
Twitter. Of the 32 astrophysicists qualified for this analysis, 10
tweeted frequently, 11 tweeted regularly, 10 tweeted occasionally
and only one tweeted rarely. Because we limited this analysis to
those usernames and those astrophysicists that were mentioned at
least 20 times, the number of astrophysicists in the last category
remained low. Based on the categories in Haustein, et al. [40], 6
astrophysicists do not publish, 9 publish occasionally, 13 publish
regularly, and 4 publish frequently. In Figure 3 the conversational
connections to users with different roles or professions are grouped
according to the tweeting activity of the astrophysicists. The results
indicate that one third of the usernames mentioned by the 10
astrophysicists who tweet frequently were science communicators,
while about one fifth were other astrophysicists. The profiles of the
conversational connections for the astrophysicists who tweet
regularly and who tweet occasionally are fairly similar, with about
one third of the mentioned usernames being other astrophysicists
and about a quarter being science communicators. Only one
astrophysicist was classified to the group that tweeted rarely;
because of this lack of data the conversational profile for this group
cannot be considered as representative. Only 3 different
usernames were mentioned in 11 tweets by the single astrophysicist
in this group.
The conversational connections based on publishing behavior
were also investigated (Figure 4). Those that publish frequently
had the most conversational connections to science communica-
tors (36.4%), while the science communicators mentioned in the
other groups were between about 27.5% and 31.3% of the total
amount of conversational connections. Another group of fre-
quently mentioned Twitter users were the other astrophysicists;
these were mentioned most by those that publish regularly
(32.5%), while in the other groups they counted for between
22.8% and 26.9% of the mentions. Various organizations and
associations related to astrophysics or astronomy (e.g. NASA, ESA)
counted for between 15.5% and 18.4% of the mentions in all
categories. All the remaining roles received some references, but
clearly less than the above-mentioned roles.
We were also interested to map who the astrophysicists
mentioned in their tweets. To map the whole communication
network around the astrophysicists was beyond the scope of this
research, as we wanted to investigate with who the astrophysicists
initiated conversations with and who their intended audience
were. The conversational connections between the 32 astrophys-
icists and those they mentioned in their tweets were visualized in a
network map (Figure 5) using the OpenOrd layout [46]. The map
shows the ego networks of the 32 astrophysicists, created from the
outgoing connections (usernames mentioned) in their tweets. The
overall graph-clustering coefficient of the network was 7.870,
density was 0.018, and average distance was 2.604. Although the
density of the graph was fairly low, the graph had high clustering
and short distances between the nodes. Both features are
frequently connected with the small world phenomenon [47]. A
community detection [43,44] in Gephi revealed seven clusters of
frequent interactions in the graph. These were colored and
indicated as Mod0–Mod6 in Figure 5. The clusters vary in size, as
they range from the smallest cluster with only 3 users (Mod6) to
the largest with 180 users (Mod3). There was also some overlap
and interaction between the different clusters.
The conversational connections within these clusters were
analyzed and differences between them were discovered (Fig-
ure 6). The astrophysicists in the first cluster (Mod0) mostly
mentioned other astrophysicists (47.1%), while the astrophysicists
in Mod4 mostly mentioned science communicators (46.7%).
Astrophysicists in Mod2 mentioned students (12.5%) and teachers
(5.0%) more than other clusters, suggesting that these astrophys-
icists use Twitter more for educational purposes. However, this
group of astrophysicists also had the most connections to other
Twitter users (40.0%) and to unknown users (17.5%). The
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 5 August 2014 | Volume 9 | Issue 8 | e106086
astrophysicists in Mod1 had the most connections to other
researchers (19.3%), possibly indicating a multidisciplinary com-
ponent in their research. All clusters, except Mod6, had
conversational connections to almost all categories of Twitter
users, again demonstrating the variety of connections that the
astrophysicists have on Twitter. It should be noted, however, that
Mod6 only contains three Twitter users, one of the observed
astrophysicists and two usernames he or she mentioned in tweets.
The content of the conversations within these clusters was
analyzed by extracting the noun phrases from the tweets. The
noun phrases from the last cluster, Mod6, were excluded from the
analysis due to the significantly lower number of tweeters. Among
the most frequently used noun phrases or words in the clusters
were words related to time (e.g. time,day,today,week,year) and
general astrophysics terms (star,planet,earth,moon,mars,science).
The similarities in the use of noun phrases and words between the
clusters were measured with Pearson’s r(Table 2). The results
indicate modest to high similarities in the choice of words and
noun phrases in the clusters, as similarities range between 0.41
(Mod2 and Mod5) to 0.78 (Mod0 and Mod3). Interestingly, the
Figure 2. Number of people contacted and the number of conversations had by the 32 astrophysicists. Figure 2 shows the number of
conversations the studied astrophysicists had with other usernames and the number of unique usernames they mentioned. Overall there is a strong
correlation between the number of users mentioned and the number of conversations, some astrophysicists have more conversations with fewer
Twitter users while others have their conversations with a much wider audience.
doi:10.1371/journal.pone.0106086.g002
Table 1. Roles of the users mentioned in the tweets.
Role or profession
Science communicator 24.13%
Other astrophysicists 21.62%
Organization or association 13.32%
Other 11.20%
Unknown 8.11%
32 astrophysicists 6.18%
Other researchers 5.98%
Teacher or educator 3.67%
Corporative 2.32%
Students 2.12%
Amateur astronomer 1.35%
doi:10.1371/journal.pone.0106086.t001
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 6 August 2014 | Volume 9 | Issue 8 | e106086
composition of the mentioned usernames is very different for
clusters with high similarities and with low similarities. The highest
similarity was between clusters Mod0 and Mod3 even though the
roles of mentioned usernames as identified through their profile
information are very different (Figure 6).
The hashtags found in the tweets in the different clusters
(Mod0–Mod6) were also analyzed separately from the tweet
content. Researchers in the different clusters used hashtags very
differently; hashtag use ranged from a total of 7 unique hashtags
used 11 times in Mod2 to over 1,000 unique hashtags used almost
4,000 times in Mod1, Mod 3 and Mod5 (Table 3). The clusters
labeled as Mod1, Mod3 and Mod5 were also very similar in their
choice of noun phrases (Table 2), but the username roles
mentioned in Mod1 were clearly different from Mod3 and
Mod5 (Figure 6).
To gain a deeper understanding of the content of the tweets sent
in each cluster, the background and meaning of the five most used
hashtags from each cluster were investigated (Table 4). The most
frequently used hashtags in the clusters labelled Mod0, Mod1,
Mod2 and Mod4 contained hashtags related to astrophysics or
astronomy; some of the hashtags are used only by a single tweeter
to label their tweets and to distinguish the tweets related to
astrophysics from their other tweets (#twinkletweet, #AstroFact),
while some are related to functionality and use of Twitter in
general (#FF, #fb). The most frequent hashtag in Mod 0 reflects
parallel use of Twitter and Facebook, presumably within the
‘‘Astronomers’’ group (https://www.facebook.com/groups/
123898011017097), which only allows professional astronomers
to join. This professional focus is corroborated by the fact that
47% of all mentioned Twitter users of Mod0 were coded as other
astrophysicists and it also contains the highest share of the 32
astrophysicists (except for Mod6). Mod1 and Mod2 reveal more
personal interactions and conversations with students. For
example, the most frequent cluster-specific noun phrases (i.e.,
terms appearing in only this and maximum one other cluster) in
Mod2 are ‘‘hug’’, ‘‘hahahaha’’, ‘‘cake’’, ‘‘revision’’ and ‘‘tea’’,
while Mod1 contains specific terms of a professor who blogs about
astronomy, physics and pop culture, often featuring his children.
Mod1 also mentions teachers, amateur astronomers and other
researchers more frequently than other clusters. Discussions in
Mod3 are related to science policy, and more specifically science
programs and funding cuts in the UK and the Science &
Technology Facilities Council (STFC) as indicated by the top five
hashtags #stfc, #scipolicy, #rcuk, #scienceisvital and #scicuts as
well as the most frequent cluster-specific noun phrases ‘‘pro-
gramme’’, ‘‘stfc’’, ‘‘item’’ and ‘‘deadline’’. The UK focus also
demonstrates a geographic connection between the tweeters in
Mod3. Hashtags in Mod5 are related to the Hubble (#Hubble)
and James Webb Space (#JWST) telescopes, NASA (#NASA)
and mathematics (#math, #mathed). In addition, some of the
hashtags from specific clusters are connected to specific conference
or workshop (e.g., #aas221, #cs17, #astro101, #clickers2012,
#scio13, #gzconf). It is possible that tweeting about conferences
have had some impact on the formation of some of the clusters
Figure 3. Percentage of people mentioned by role by astrophysicist on average by tweeting behavior. Figure 3 shows the
conversational connections to users with different roles or professions according to the tweeting activity of the astrophysicists.
doi:10.1371/journal.pone.0106086.g003
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 7 August 2014 | Volume 9 | Issue 8 | e106086
that were detected, emphasizing the fact that the clusters detected
in this research consist of people that share similar interests.
A positive and a negative sentiment value and a combined
sentiment score of the tweets from each cluster were measured (as
shown in Table 5). Because it was discovered that 449 tweets did
not have any content (or only contained hashtags), the total
number of tweets used in the sentiment analysis was 67,783. None
of the clusters showed strong positive or strong negative
sentiments, as the mean values range between 1.825 and 2
1.509 (range of possible values being between 5 and 1 for positive
values and 21 and 25 for negative values). The combined mean
sentiment scores range between 0.316 (Mod2) and 20.071
(Mod4). The sentiment of the tweets in the clusters labeled as
Mod0 and Mod2 were somewhat more positive, while the
sentiment of the tweets in Mod4 and Mod5 were virtually neutral.
Interestingly there was a negative correlation between the
sentiment score of the clusters and the number of tweets sent. A
Spearman correlation of 20.371 (p = 0.497) suggests that in the
clusters where more tweets were sent the sentiment of the tweets
were closer to neutral, while clusters where fewer tweets were sent
the tweets were somewhat more positive.
Discussion
The results of this work indicate that the astrophysicists in this
study are in conversational connections with a wide variety of
other Twitter users, although some difference in the usage can be
identified. As noted earlier, Haustein, et al. [40] found that
astrophysicists who tweet frequently do not publish frequently and
vice versa. Our results indicate that astrophysicists who tweet
frequently mention science communicators more than other
astrophysicists and other researchers, which is a behavior differing
from those who tweet less frequently. This suggests that
astrophysicists who frequently tweet do so for reasons other than
to communicate directly with colleagues. Interestingly, both
frequent tweeters and frequent publishers often mention science
communicators in their tweets. Although not confirmed by the
results in this study it could be that those who publish frequently
maintain more conversational connections to science communi-
cators in order to disseminate research results to a wider audience,
while those who tweet frequently do so to share information about
astrophysics in general, rather than specifically to discuss or
promote their own research. It is, however, noteworthy to
highlight the fact that astrophysicists, no matter their publishing
Figure 4. Average of people mentioned by role by astrophysicist by publishing behavior. Figure 4 shows the conversational connections
to users with different roles or professions based on the publishing behavior of the studied astrophysicists.
doi:10.1371/journal.pone.0106086.g004
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 8 August 2014 | Volume 9 | Issue 8 | e106086
behavior or tweeting behavior, have plenty of conversational
connections with Twitter users of varying roles and professions.
A small world graph emerged from the visualization of the
conversational connections (outgoing ego network based on the
conversations initiated or usernames mentioned by the astrophys-
icists) between the researched astrophysicists and other Twitter
users as indicated by dense local clusters and short distances
between the nodes in the graph. A closer look at the conversational
connections revealed some differences in the connections in the
clusters. One cluster clearly had more conversational connections
to other astrophysicists (outside the group of astrophysicists studied
in this research), while another cluster had more connections to
science communicators. One of the clusters had more connections
to researchers in other research areas than astrophysics or
astronomy, possibly indicating an interdisciplinary component to
their research. Another cluster had more connections to students
and teachers, possibly suggesting that these astrophysicists use
Twitter more for educational purposes. The results showed a great
variation in the professional composition of the clusters created by
conversational connections. Interestingly the community detection
did not just discover clusters of people with more frequent
conversational connections to each other, but it also discovered
clusters of people with the same professional roles. It is not clear
what roles these connections play, if any, between those using
Twitter for personal reasons as compared to those using Twitter
for professional reasons; more research is needed to examine any
differences.
Figure 5. Conversational connections in the astrophysicists’ tweets. The network graph in figure 5 shows the conversational connections of
the astrophysicists and the communities in them as detected with Gephi’s community detection.
doi:10.1371/journal.pone.0106086.g005
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 9 August 2014 | Volume 9 | Issue 8 | e106086
Although the conversational connections revealed distinct
clusters, it is striking that these clusters often use the same words
and hashtags when tweeting. We have shown that the professional
roles in the clusters are very different, yet the content of their
tweets are very similar. The differences in the professional
composition of the clusters suggest that although the content of
the tweets are very similar, the motivations for tweeting are
different because the intended target audiences are different. For
altmetric research this raises some questions: As a measure of
research visibility, are all tweets equal? Are all mentions equal?
Are the altmetric indicators always created in a specific type of
conversational context? What affordances and norms do scholars
utilize to distinguish personal and professional tweets and can
altmetric indicators discriminate between the two roles?
Another question that arises from the results is why the clusters
are not more connected to each other if they are interested in the
same topics? One reason for that might be that mentioning
someone in a tweet reflects a real-world network and are simply
conversations between friends or colleagues rather than pure
conversational networks based on topics, like Gruzd, Wellman,
and Takhteye [48] have suggested. The content of the tweets also
suggests another reason. The majority of the tweets do not contain
Figure 6. Percentage of people with different roles in the 7 communities. Figure 6 shows the professional make-up of the communities
detected in the conversational network. The results show how the different conversational communities consist of very different types of users.
doi:10.1371/journal.pone.0106086.g006
Table 2. Similarities (Pearson’s r) between the noun phrases used in each community (Mod0–Mod6).
Mod0 Mod1 Mod2 Mod3 Mod4 Mod5
Mod0 0.66 0.55 0.78 0.74 0.60
Mod1 0.66 0.68 0.73 0.70 0.64
Mod2 0.55 0.68 0.55 0.59 0.41
Mod3 0.78 0.73 0.55 0.72 0.75
Mod4 0.74 0.70 0.59 0.72 0.68
Mod5 0.60 0.64 0.41 0.75 0.68
doi:10.1371/journal.pone.0106086.t002
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 10 August 2014 | Volume 9 | Issue 8 | e106086
Table 3. Number of hashtags and unique hashtags used in the tweets of the detected communities.
Mods Total hashtags Unique hashtags
Mod0 2,569 633
Mod1 3,748 1,215
Mod2 321 184
Mod3 3,977 1,074
Mod4 1,656 564
Mod5 3,862 1,350
Mod6 11 7
doi:10.1371/journal.pone.0106086.t003
Table 4. Top 5 hashtags and their meaning by community.
Cluster Hashtag (frequency) Explanation
Mod0 #fb (519) Indicates tweets that are automatically imported to Facebook
Mod0 #twinkletweet (284) A tag used by an astrophysics professor to distinguish his personal tweets from professional tweets
Mod0 #dotastro (139) ‘‘Astronomy aims to bring together an international community of astronomy researchers, developers, educators and
communicators to showcase and build upon these many web-based projects, from outreach and education to research tools
and data analysis’’ (http://dotastronomy.com/about/)
Mod0 #aas221 (81) American Astronomical Society 221st Program
Mod0 #cs17 (81) 17th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun
Mod1 #AstroFact (348) A tag used by an astronomy professor to tweet astronomy facts and distinguish these facts from other tweets
Mod1 #astro101 (142) Colloquium at CAPER Center for Astronomy & Physics Education Research
Mod1 #clickers (105) Clickers and other classroom technologies can enable institutions and faculty to respond to the transformation of the learning
environment into an interactive space
Mod1 #clickers2012 (103) Clickers Conference, 2012, Chicago
Mod1 #scio13 (86) ScienceOnline2013, 7th annual international meeting on Science and the Web
Mod2 #gzconf (22) Galaxy Zoo conference
Mod2 #FGM (19) Female Genital Mutilation, Reaction to a campaign against FGM which was the subject of a Channel 4 documentary, The Cruel
Cut, which features the shocking scenes where Leyla Hussein (co-founder of the anti-FGM charity Daughters of Eve) asks
people to sign the petition.
Mod2 #hugs (18) Expressing emotion
Mod2 #NHS (14) National Health Service, UK
Mod2 #FF (13) Follow Friday: Tweet the names of Twitter users you’d like others to follow and tag it with followfriday and/or FF
Mod3 #stfc (409) Science & Technologies Facility Council, UK
Mod3 #scipolicy (216) Science Policy, UK
Mod3 #rcuk (143) Research Council UK
Mod3 #scienceisvital (122) ‘‘We are a group of concerned scientists, engineers and supporters of science who are campaigning to prevent destructive
levels of cuts to science funding in the UK’’ (scienceisvital.org.uk).
Mod3 #scicuts (94) Belongs to #scienceisvital
Mod4 #AAS218 (188) American Astronomical Society 218
th
Program
Mod4 #PS1 (95) PS1 Prototype Telescope on Haleakala, Maui.
Mod4 #NucATown (79) Nuclear Astrophysics Town Meeting
Mod4 #FF (48) Follow Friday: Tweet the names of Twitter users you’d like others to follow and tag it with followfriday and/or FF
Mod4 #astrojc (44) Astronomy Twitter Journal Club where people meet up on Twitter at a prearranged day and time and discuss an interesting
piece of astronomy research
Mod5 #Math (330) Mathematics
Mod5 #JWST (256) James Webb Space Telescope
Mod5 #nasa (202) National Aeronautics and Space Administration
Mod5 #Hubble (154) Hubble space telescope
Mod5 #mathed (152) Mathematics education
doi:10.1371/journal.pone.0106086.t004
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 11 August 2014 | Volume 9 | Issue 8 | e106086
jargon specific to astrophysics, but rather astrophysics on a more
general level. This suggests that the astrophysicists have framed
their communications so that they can connect with diverse
audiences, as suggested for instance by Niset [7] and Groth and
Gurney [6]. More specialized discussions between astrophysicists
and astronomers might appear in the Astronomers group on
Facebook mentioned above. The affordances of Twitter, including
the limitation of 140 characters, the use of hashtags, mentions, and
retweets, and the limited profile information, may also contribute
to the disconnect between clusters because of the way in which the
tweets are framed by the various actors in the network; more
research is needed on Twitter affordance use and framing.
The sentiment analysis of tweets resulted in low sentiment scores
for positive sentiments and negative sentiments for all clusters,
although there was one cluster (Mod3) that discussed budget cuts
in science funding and was perhaps expected to produce negative
sentiments. A study of brand related tweets [49] found that two
thirds of tweets contain positive sentiments, suggesting that Twitter
users rather tend to tweet positive expressions than negative. That
phenomenon might be supported by our next finding, that a
connection was discovered between the numbers of tweets sent in
a cluster and the sentiment of those tweets. The results showed
that in the clusters where more tweets were sent the tweets tend to
be more neutral, in contrast to somewhat more positive tweets in
the clusters where fewer tweets were sent. Since [26] could show
that the expression of sentiments does not depend on the tweeting
frequency, this suggests that those astrophysicists that tweet
frequently do so mainly to share information, not to express their
own opinion. However, given that the analyzed clusters varied in
size and tweeting frequency we present tendencies instead of
statistically significant correlations.
The present study is not completely without limitations. The
research is limited by its small sample size (tweets from 32
astrophysicists) and, as such, gives some first insights into the
astrophysicists tweeting behavior for scholarly communication. In
future work we will extend the analysis to researchers from
different disciplines to examine whether there are discipline-
specific conversation strategies in scholarly communication on
Twitter. In content analysis and manual coding of objects, the
coding should ideally be done by at least two people and inter-
coder reliability (e.g. Cohen’s kappa) should be calculated. Coding
of the usernames by professional types in this research was done by
one author based on information found on the Twitter profiles of
each user. Although in most cases the coding was fairly
straightforward with not much room for interpretation (e.g.
‘‘Astrophysics Professor at X’’), there were some ambiguous cases
(e.g. ‘‘Assistant professor in astrophysics, science blogger, teach-
er’’). In cases where more than one role could have fitted the user,
we chose to code the user based on the first role the user
mentioned. Another limitation that needs to be acknowledged is
the use of the community detection. We used the built-in
community detection in Gephi [43,44], but there are other
algorithms that could have been used too and that could have
taken into account that a user may simultaneously belong to two
or more different communities or clusters. Although this was
beyond the scope of this research, an interesting future direction
could be to focus on the users that have multiple roles and that
simultaneously belong to two or more clusters.
Author Contributions
Conceived and designed the experiments: KH TDB SH IP. Analyzed the
data: KH TDB SH IP. Contributed to the writing of the manuscript: KH
TDB SH IP.
References
1. For Citizen Scientists - NASA Science. (n.d.) Available: http://science.nasa.gov/
citizen-scientists/. Accessed 2014 May 22.
2. Christian C, Lintott C, Smith A, Fortson L, Bamford S (2012) Citizen Science:
Contributions to Astronomy Research. In: Heck A, editor. Organizations,
People and Strategies in Astronomy. Duttlenheim: Venngeist. 183–197.
3. Kouper I (2010) Science Blogs and Public Engagement with Science: Practices,
challenges, and opportunities. Journal of Science Communication, 9: 1–10.
4. Blood R (2002) The Weblog Handbook: Practical Advice on Creating and
Maintaining Your Blog. Cambridge, Ma: Perseus Publishing.
5. Bonetta L (2007) Scientists Enter the Blogosphere. Cell, 129: 443–445.
6. Groth P, Gurney T (2010) Studying Scientific Discourse on the Web using
Bibliometrics: A Chemistry Blogging Case Study. WebSci10, Raleigh, NC, US.
Available: http://journal.webscience.org/308/2/websci10_subm ission_48.pdf.
Accessed 2014 May 22.
7. Niset MC (2009) Framing Science: A New Agenda in Public Engagement. In
Kahlor L, Stout PA, editors. Communicating Science: New Agendas in
Communication. New York, NY: Routledge. 40–67.
8. Priem J, Groth P, Taraborelli D (2012) The Altmetrics Collection. PLoS ONE
7(11): e48753. doi:10.1371/journal.pone.0048753.
9. Thelwall M, Haustein S, Larivie`re V, Sugimoto CR (2013) Do Altmetrics Work?
Twitter and Ten Other Social Web Services. PLoS ONE 8(5): e64841.
doi:10.1371/journal.pone.0064841.
10. Haustein S, Larivie`re V, Thelwall M, Amyot D, Peters I (in press) Tweets vs.
Mendeley Readers: How Do these Two Social Media Metrics Differ? IT-
Information Technology.
11. Priem J, Costello KL (2010) How and Why Scholars Cite on Twitter. American
Society for Information Science and Technology (ASIST 2010). doi:10.1002/
meet.14504701201. 1–4.
12. Chretien KC, Azar J, Kind T (2011) Physicians on Twitter. JAMA. 305: 566–
568. doi:10.1001/jama.2011.68.
13. Holmberg K, Thelwall M (2014) Disciplinary Differences in Twitter Scholarly
Communication. Scientometrics. doi:10.1007/s11192-014-1229-3.
14. Rowlands I, Nicholas D, Russell B, Canty N, Watkinson A (2011) Social Media
Use in the Research Workflow. Learn Publ, 24(3). doi:10.1087/20110306.
Table 5. Sentiment of tweets by communities.
Mod Mean positive Mean negative Sentiment score Sum of tweets
Mod0 1.596 21.294 0.302 7481
Mod1 1.656 21.468 0.188 12127
Mod2 1.825 21.509 0.316 3255
Mod3 1.582 21.337 0.245 17709
Mod4 1.386 21.458 20.071 6033
Mod5 1.448 21.343 0.104 20735
Mean 1.582 21.387 0.194 9683
doi:10.1371/journal.pone.0106086.t005
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 12 August 2014 | Volume 9 | Issue 8 | e106086
15. Ponte D, Simon J (2011) Scholarly Communication 2.0: Exploring Researchers’
Opinions on Web 2.0 for Scientific Knowledge Creation, Evaluation and
Dissemination. Serials Review, 37(3). doi:10.1016/j.serrev.2011.06.002.
16. Moran M, Seaman J, Tinti-Kane H (2011) Teaching, Learning, and Sharing:
How Today’s higher Education Faculty Use Social Media. Available: http://
www.pearsonlearningsolutions.com/educators/pearson-social-media-survey-
2011-bw.pdf. Accessed 2014 May 22.
17. Brenner J, Smith A (2013) 72% of Online Adults are Social Networking Site
Users. Available: http://pewinternet.org/Reports/2013/social-networking-sites.
aspx. Accessed 2014 May 22.
18. Priem J, Costello K, Dzuba T (2011) Prevalence and Use of Twitter Among
Scholars. Metrics 2011: Symposium on Informetric and Scientometric Research.
New Orleans, LA, USA, October 12. Available: http://figshare.com/articles/
Prevalence_and_use_of_Twitter_among_scholars/104629. Accessed 2014 May
22.
19. Haustein S, Peters I, Bar-Ilan J, Priem J, Shema H, et al. (2014) Coverage and
Adoption of Altmetrics Sources in the Bibliometric Community. Scientometrics.
doi:10.1007/s11192-013-1221-3.
20. Bowman TD, Demarest B, Weingart SB, Simpson GL, Larivie` re V, et al. (2013)
Mapping DH through Heterogeneous Communicative Practices. Digital
Humanities 2013. Lincoln, NE.
21. Pscheida D, Albrecht S, Herbst S, Minet C, Ko¨hler T (2014) Nutzung von Social
Media und onlinebasierten Anwendungen in der Wissenschaft. Erste Ergebnisse
des Science 2.0-Survey 2013 des Leibniz-Forschungsverbunds ‘‘Science 2.0’’.
Available: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-132962. Accessed
2014 Apr 7.
22. Honeycutt C, Herring SC (2009) Beyond Microblogging: Conversation and
Collaboration via Twitter. 42
nd
Hawaii International Conference on System
Sciences. Available: http://ella.slis.indiana.edu/,herring/honeycutt.her ring.
2009.pdf. Accessed 2013 Jul 5.
23. Zhao D, Rosson MB (2009) How and Why People Twitter: The Role that
Micro-blogging Plays in Informal Communication at Work. ACM 2009
International Conference on Supporting Group Work. Available: http://www.
pensivepuffin.com/dwmcphd/syllabi/info447_au10/readings/zhao.rosson.
Twitter.GROUP09.pdf. Accessed 2013 Jul 5.
24. Bruns A, Stiegli tz S (2012) Quantitative Approaches to Comparing Commu-
nication Patterns on Twitter. J Technol Hum Ser, 30: 160–185.
25. Weller K, Dro¨ge E, Puschmann C (2011) Citation Analysis in Twitter:
Approaches for Defining and Measuring Information Flows within Tweets
during Scientific Conferences. In Rowe M, Stankovic M, Dadzie A-S, Hardey
M, editors. Making Sense of Microposts (#MSM2011), Workshop at Extended
Semantic Web Conference (ESWC 2011), Crete, Greece. CEUR Workshop
Proceedings Vol. 718. 1–12.
26. Stieglitz S, Kru¨ger N (2011) Analysis of Sentiments in Corporate Twitter
Communication – A Case Study on an Issue of. 22nd Australasian Conference
on Information Systems, Sydney, Australia. Available: http://aisel.aisnet.org/
acis2011/29. Accessed 2014 May 22.
27. Stieglitz S, Dang-Xuan L (2012) Political Communication and Influence
through Microblog ging. An Empirical An alysis of Sentiment in Twitter
Messages and Retweet Behavior. 45th Hawaii International Conference on
System Sciences (HICSS). doi:10.1109/HICSS.2012.476.
28. Bifet A, Frank E (2010) Sentiment Knowledge Discovery in Twitter Streaming
Data. In: Pfahringer B, Holmes G, Hoffmann A, editors. 13th International
Conference on Discovery Science, Canberra, Australia. Berlin, Heidelberg:
Springer. 1–15.
29. Veltri GA (2012) Microblogging and Nanotweets: Nanotechnolog y on Twitter.
Publ Underst Sci, 22: 832–849.
30. Thelwall M (in press) Heart and Soul: Sentiment Strength Detection in the
Social Web with SentiStrength. Available: http://sentistreng th.wlv.ac.uk/
documentation/SentiStrengthChapter.pdf. Accessed 2014 May 22.
31. Verlic M, Stiglic G, Kocbek S, Kokol P (2008) Sentiment in Science: A Case
Study of CBMS Contributions in Years 2003 to 2007. 21
st
IEEE International
Symposium on Computer-based Medical Systems, Jyva¨skyla¨ , Finland. IEEE
Computer Society: Washington, DC, USA. 138–143.
32. Small H (2010) Interpreting Maps of Science Using Citation Context
Sentiments: A Preliminary Investigation. Scientometrics, 87: 373–388.
33. Athar A, Teufel S (2012) Context-enhanced Citation Sentiment Detection. 2012
Conference of the North American Chapter of the Association for Computa-
tional Linguistics: Human Language Technologies. Stroudsburg, PA, USA:
ACM. 597–601.
34. Harwood N (2009) An Interview Based Study of the Functions of Citations in
Academic Writing Across Two Disciplines. J Pragmat, 41: 497–518.
35. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment
Strength Detection in Short Informal Text. J Am Soc Inf Sci Technol, 61: 2544–
2558.
36. Eysenbach G (2011) Can Tweets Predict Citations? Metrics of Social Impact
Based on Twitter and Correlation with Traditional Metrics of Scientific Impact.
J Med Internet Res, 13. Available: http://www.jmir.org/2011/4/e123/.
Accessed 2012 Jul 7.
37. Shuai X, Pepe A, Bollen J (2012) How the Scientific Community Reacts to
Newly Submitted Preprints: Article Downloads, Twitter Mentions, and
Citations. PLoS ONE, 7(11): e47523. doi:10.1371/journal.pone.0047523.
38. Haustein S, Peters I, Sugim oto CR, Thelwall M, Larivie`re V (2014) Tweeting
biomedicine: An analysis of tweets and citations in the biomedical literature.
J Am Soc Inf Sci Technol, 64: 656–669.
39.HausteinS,BowmanTD,HolmbergK,Larivie`re V, Peters I (2014)
Astrophysicists on Twitter: An In-depth Analysis of Tweeting and Scientific
Publication Behavior. Aslib Journal of Information Management, 66: 279–296.
40. Haustein S, Bowman TD, Macaluso B, Sugimoto CR, Larivie`re V (2014)
Measuring Twitter activity of arXiv e-prints and published papers. altmetrics14:
expanding impacts and metrics. An ACM Web Science Conference 2014
Workshop. Bloomington, IN. doi:10.6084/m9.figshare.1041514.
41. Thelwall M (2009) Introduction to Webometrics: Quantitative Web Research
for the Social Science s. Synth Lect Inf Concepts Retr Serv, 1: 1–116
doi:10.2200/S00176ED1V01Y200903ICR004. San Rafael, CA: Morgan &
Claypool.
42. Bastian M, Heymann S, Jacomy M (2009) Gephi: An Open Source Software for
Exploring and Manipulating Networks. ICWSM. Available: http://gephi.org/
publications/gephi-bastian-feb09.pdf. Accessed 2014 May 22.
43. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast Unfolding of
Communities in Large Networks. J Stat Mech Theory Exp, P10008
doi:10.1088/1742-5468/2008/10/P10008.
44. Lambiotte R, Delvenne J-C, Barahona M (2009) Laplacian Dynamics and
Multiscale Modular Structure in Networks. Available: http://arxiv.org/abs/
0812.1770. Accessed 2014 May 22.
45. Van Eck NJ, Waltman L, Noyons ECM, Buter RK (2010) Automatic Term
Identification for Bibliometric Mapping. Scientometrics, 82: 581–596.
46. Martin S, Brown WM, Klavans R, Boyack K (2011) OpenOrd: An Open-
Source Toolbox for Large Graph Layout. SPIE Conference on Visualization
and Data Analysis (VDA). doi:10.1117/12.871402.
47. Watts DJ, Strogatz SH (1998) Collective Dynamics of ‘Small-world’ Networks.
Nature, 393: 440–442.
48. Gruzd A, Wellman B, Takhteye Y (2011) Imagining Twitter as an Imagined
Community. Am Behav Sci, 55: 1294–1318.
49. Jansen BJ, Zhang MM, Sobel K, Chowdury A (2009) Twitter Power: Tweets as
Electronic Word of Mouth. J Am Soc Inf Sci Technol, 60: 2169–2188.
Tweet Construction, Affordance Use, and Conversational Networks
PLOS ONE | www.plosone.org 13 August 2014 | Volume 9 | Issue 8 | e106086
