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DOI: 10.4018/IJSWIS.2020070106
Volume 16 • Issue 3 • July-September 2020
Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
88
Saleha Noor, School of Information Science and Engineering, East China Science and Technology University, China
https://orcid.org/0000-0002-0043-4189
Yi Guo, School of Information Science and Engineering, East China University of Science and Technology, China
https://orcid.org/0000-0002-6088-7198
Syed Hamad Hassan Shah, Glorious Sun School of Business and Management, Donghua University, China
https://orcid.org/0000-0001-9043-4811
M. Saqib Nawaz, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
https://orcid.org/0000-0001-9856-2885
Atif Saleem Butt, American University of Ras Al Khaimah, UAE
In literature, there is a shortage of comprehensive documents that can provide proper details about
Twitter in research community. This study conducted a first descriptive bibliometric analysis to
examine the most influential journals, institutions, and countries on Twitter. Similarly, bibliometric
mapping analysis is carried out to explore different research themes in Twitter publications. VOSviewer
was employed to process the 11,006 Twitter publications retrieved from the Web of Science (WoS)
from 2009 to 2018. Obtained results suggest that USA and China received the highest number of
publications on Twitter research, while the University of Illinois was the most productive institute.
Furthermore, the five major themes have emerged in Twitter publications, and its remarkable role has
been found in event detection, sentiment analysis, education, health, politics, and crisis as well as risk
management. The authors believe that this study will open new doors for researchers to use online
Twitter social networking communities in beauty salons, consulting companies, banks, and airlines.
Bibliometric Analysis, Thematic Analysis, Twitter, VOSviewer
The social networking site Twitter is used by approximately 326 Million users every month and each
day, approximately 500 Million tweets are sent (Twitter Stats, 2019). Users on Twitter constantly
take effective part in any breaking news or mega events that happen around the world. Twitter users
can follow other users or can be followed. No reciprocation is required in Twitter like other social
networking sites, e.g. Facebook or MySpace (Benetoli et al., 2015). On Twitter, the messages that
user receives from other users are called tweets. The response to these tweets is known as retweets
and the symbol ‘@’ is used to address the other user while a hashtag ‘#’ is used to follow a keyword.
Only 140 characters are allowed to write a Twitter message that is why Twitter is also well known
Volume 16 • Issue 3 • July-September 2020
89
as micro-blogging social networking site. This process strengthens Twitter users to fan out precise
and relevant information beyond the original tweet’s followers.
Prior studies have elucidated the significance of Twitter in numerous research fields, such
as health (Grover et al., 2018; Nawaz et al., 2017; Jacobson & Mascaro, 2016), education (Hull
& Dodd, 2017; Forgie et al., 2013; Kassens-Noor, 2012), social preference learning (Kwon &
Lee, 2013), tourism (Sotiriadis & van Zyl, 2013), management (IPCC, 2013), sarcastic sentiment
detection (Bharti et al., 2017), event detection (Hasan et al., 2018), users interest detection (Shahzad
et al., 2017), politics (Guerrero-Solé, 2018; Di Fatta & Yolles, 2017; McGregor & Mourão, 2016;
Hopke, 2015) and sports (Mustafa et al., 2017; Williams et al., 2014). All these studies reflect the
significant involvement of Twitter in several prominent sectors. However, there are scarcest studies
in the previous literature that provide an overall contribution of Twitter. The number of publications
related to Twitter is dramatically increasing and the total number is more than 12000 only in the
Web of Science (WoS) database till April 2019. This makes it inevitable to investigate more about
who are the most competitive and authoritative journals, countries and organizations that are playing
a vital role in the Twitter studies. Therefore, a bibliometric analysis of Twitter is conducted in this
study to determine the most competitive and authoritative journals, countries and organizations for
Twitter studies. Furthermore, we also explored the related themes of Twitter through co-occurrence
of keywords found in the title, abstract and author(s) keywords in Twitter publications.
We searched for bibliometric studies of social media, particularly Twitter in WoS. Consequently,
we found only 22 studies as shown in Appendix 1. After analyzing them, we found that most of these
studies discussed only specific topics related to Twitter or social media in bibliometric analysis,
e.g., bibliometric analysis of highly cited articles in Twitter: case study of biology (Zhang & Wang,
2018), social media bibliometric analysis of information and library science (Gan & Wang, 2014),
bibliometric of neurosurgical journals and departments (Alotaibi et al., 2016), bibliometric analysis
of social media in psychology (Zyoud et al., 2018), bibliometric of event detection (Chen et al., 2019)
and the role of social media in innovation research through bibliometric approach (Appio et al., 2016).
However, no study is found in literature according to our best knowledge that explores the overall
contribution of Twitter in research academia (most productive journals, institutes and countries in
Twitter research) and can inform the researchers where Twitter has been used (thematic analysis).
We found only two bibliometric studies of social media publications that is closely related to
this study. First, Li et al. (2017) conducted bibliometric of social media publications, during the time
period of 2008–2014, based on the Social Science Citation Index (SSCI) and Science Citation Index
(SCI) databases. With 10,042 social media articles, this study explored subject categories, major
journals, international collaboration, temporal evolution in authors’ keywords and spatial distribution
in social media studies. Second, Kapoor et al. (2017) conducted a bibliometric study of 12000 social
media publications between 1997-2017 using VOSviewer. They found seven major themes through
author co-citation analysis and five themes through text analysis in social media publications. The
second study also indicated that most of research papers related to information seeking during critical
events and natural disasters focused on Twitter data.
This sparked our interest to determine the sole contribution of Twitter in the social media academic
research. Despite of inevitable significance of citation links, bibliographic coupling links and co-
authorship links in bibliometric analysis (Perianes et al., 2016), these bibliometric indicators have
not been used explicitly in the literature to explore the most productive and influential countries and
institutions in Twitter research. To fulfill this gap, this study reviewed relevant Twitter articles through
the bibliometric analysis to elucidate the research dynamics through citation links, bibliographic
coupling links, co-authorship links and key research themes of Twitter publications through co-
occurrence analysis. We believe that this study will serve as a one-stop source to offer more insights
into Twitter research regarding what is accomplished so far and what opportunities and challenges
lie ahead. Through this study, we were able to find the answers for the following research questions:
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90
• What is the yearly growth output of Twitter publications during 2009 – 2018?
• What is the distribution of Twitter publications under the umbrella of subject categories?
• Which are the most influential sources (journals and conferences) in Twitter publications indexed
by WoS?
• Which are the most productive countries and organizations on the basis of citation, bibliographic
coupling and co-authorship in Twitter publications?
• What are the emerging themes and hotspots in Twitter publications?
This study continues with the following structure. Section 2 begins with the bibliometric overview
and prominence of VOSviewer as mapping tool. Section 3 provides the details of the methodology
that we used for the bibliometric analysis. Next, the results are discussed in tabular form and with a
series of visual representations showing the emerging research clusters through VOSviewer in Section
4. Section 5 introduces key highlights of our study and implications for academia and society. Finally,
this study is concluded with limitations and recommendations for future research in Section 6.
Bibliometric studies have always been state of the art due to contributions in making certain area of
interest known. Bibliometric studies offer so much diversity in order to comprehend the relevance
of all studies. Pritchard (1969) defined bibliometric studies as a way of applying statistics and
mathematics to understand the nature, evolution and growth of an underlying research discipline. The
content analysis and citation analysis were focused in the traditional bibliometric methods (Zupic
& Čater, 2015). The emerging bibliometric studies mainly focus on network analysis to explore the
relationships among countries (Bonilla et al., 2015), research institutes (Coupé, 2003), authors (Eck
& Waltman, 2008) and keywords (Ding et al., 2001). In previous literature diverse disciplines have
used VOSviewer to conduct bibliometric analysis, e.g. business intelligence (Liang & Liu, 2018),
health (Kokol et al., 2018), Social Media in Knowledge Management (Noor et al., 2020), psychology
(Zyoud et al., 2018), brand personality analysis (Llanos-Herrera & Merigo, 2019), prosumption (Shah
et al., 2019; Shah et al., 2020) and nursing (Davidson et al., 2014).
In bibliometric studies, several indicators such as the citations, total publications, co-citation
links, bibliographic coupling links, co-authorship links and co-occurring of keywords are used
to measure the prominence and significance of research dynamics such as the most influential
journals, authors, articles, institutions and countries (Eck & Waltman, 2014; Garfield, 1955). Two
main bibliometric perspectives have been considered in research academia to evaluate the research
dynamics conventionally: (a) productivity and (b) influence. Productivity is usually measured with total
number of documents, while the influence by total citations (Podsakoff et al., 2008). However, there
are other emerging indicators like co-citation links, bibliographic coupling links and co-authorship
links that may be used in citation mapping to formulate a theme or cluster (Eck & Waltman, 2010).
In this study, we have applied several of them and illustrated in respective sections so each reader
can have information according to his/her best interest. These links are based on natural language
processing software (VOSviewer) (Eck & Waltman, 2010) that demonstrates high reliability and
reproducibility of thematic clustering while removing manual text analysis expectation biases (Al-
Barakati & Daud, 2018).
Prior studies used VOSviewer in different resaerch areas to conduct bibliometric mapping, e.g.,
business intelligence (Liang & Liu, 2018), health (Kokol et al., 2018), psychology (Zyoud et al.,
2018), brand personality (Llanos-Herrera & Merigo, 2019) and Nursing (Davidson et al., 2014). Pan
et al. (2018) investigated 481 research articles that used different bibliometric mapping software tools
and found that VOSviewer was used more frequently than other bibliometric software. VOSviewer
represents enormous information in a single visual networking map based on the visualization of
similarities (VOS) technique (Eck & Waltman, 2009). In fact, VOSviewer creates clusters of authors
Volume 16 • Issue 3 • July-September 2020
91
keywords, countries and organizations on the basis of citations and bibliographic coupling links. These
clusters represent closeness of articles, countries, organizations and keywords in specific research
stream and help to explore the diverse dimensions of a specific discipline.
The development of this study is based on the bibliometric analysis and is largely inspired by the
methodologies used by Kokol et al. (2018) and Kapoor et al. (2017). This methdology have also been
applied to a number of jounals to furnish one-stop study that provides an overview of journal insights
(Tang et al., 2018; Vošner et al., 2016) and diverse displines such as E-health (Muller et al., 2018),
social media (Li et al., 2017), prosumption (Shah et al., 2019) and sport management (Shilbury, 2016).
To identify and retrieve relevant articles for inclusion, we employed a screening routine in WoS
as employed by Llanos-Herrera and Merigo (2019). This allowed us to identify the best optimal
research publications. WoS is a top quality database comprising top journals of basic Science, social
science, arts and humanities disciplines. It contains more than 22,000 journals, data from 50 million
publications in 70 languages and 151 research categories. Therefore, this study is focused on the WoS
database despite of other research engines, such as Google Scholar, SCOPUS or Scientific Electronic
Library Online (SciELO).
We started by query string for the topic “Twitter” in the WoS Core Collection database and received
11,219 articles. This database covered timespan of years (2009- November 2018) and consisted of
SCI-Expanded, SSCI and Conference Proceedings Citation Index - Science (CPCI-S). 11,219 Twitter
publication database were comprised of 14 languages named as English (11,006), Spanish (121),
Turkish (30), Portuguese (14), French (12), German (12), Dutch (11), Slovenian (3), Afrikaans (2),
Norwegian (2), Chinese (2), Czech (2), Hungarian (1) and Korean (1). As we were only interested in
English language, therefore we excluded other languages publications from the dataset.
First of all, we extracted dataset files comprising of 500 Twitter publications each file, as WoS allows
only 500 publications to retrieve at a time. Consequently, we have 11006 dataset in 23 text document
files. These text files were comprised of authors’ name, title of article, journals’ name, language used
in writing paper, type of document, abstract and cited reference used. This all information is really
valuable for bibliometric analysis. After that, we imported these files in VOSviewer to conduct the
citation analysis, bibliographic coupling, co-authorship analysis and co-occurrence keyword analysis as
output information. Form these outputs, we explored the most productive countries and organizations
in Twitter publications and the related research streams of Twitter publications.
In this section, obtained results are discussed in detail.
The 11006 Twitter publications outputs per year are illustrated in Figure 1. In 2009, annual growth
rate of Twitter publications exceeded seven times more from 12 (2008) to 82 publications. After that,
annual Twitter publications were increased dramatically from 202 in 2010 to 1045 in 2013 depicting
marvelous development in Twitter research. Just after three years, it crossed 2000 annual publications
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92
milestone in 2016. In 2017, it showed steady behaviour exhibiting 2404 annual publications but in
2018, a declining behaviour has been observed in Twitter publications as shown in Figure 1. There
may be two possible reasons for this. First, we extracted Twitter publications data in November 2018,
not covering the full Twitter publications for the whole 2018 year. Second, WoS does not incorporate
the data in real time, so by that time there must be many articles in 2018 which would not had been
indexed by WoS.
11006 Twitter publications were distributed by 125 WoS-defined subject categories. The top
20 subject categories are represented in Table 1. The most common categories are computer science
(5631 papers; 51.163% of total publications), engineering (2162; 19.644%), communication (1028;
9.340%), information science library (736; 6.687%) and telecommunications (663; 6.024%). Among
all these top five mentioned categories, computer science had a leading behaviour and rapid growth
rate. Articles that belong to these top 20 categories contributed 93% of 11006 Twitter publications.
Table 1 shows that the Twitter research is related to diverse range of disciplines, but mainly research
has been done in top five categories.
These 11006 articles that were published in 2009-2018 appeared in 4462 WoS sources (journals and
proceedings). The top 10 most influential journals and proceedings are summarized in Table 2. Four
out of them were indexed by SSCI, while 5 were ranked as Conference Proceedings Citation Index
(SPCI) and only one journal was indexed as SCIE. These 10 out of the 4462 sources had published
15.5% of the total publications. The Lecture Notes in Computer Science (CPCI) was the leading source
with 555 publications on Twitter. PLOS ONE was ranked second with 219 publications, followed
by Computers in Human Behavior (155), Lecture Notes in Artificial Intelligence (149), Journal of
Medical Internet Research (123), Information Communication & Society (114), Procedia Computer
Science (93), New Media & Society (89), Advances in Intelligent Systems and Computing (81), and
Communications in Computer and Information Science (81) respectively. It is important to point
Figure 1. Yearly growth output of Twitter publications
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93
out that Lecture Notes in Computer Science (LNCS) is not a single source, rather a Springer series
which mostly publishes proceedings of different conferences. This might be a reason, LNCS is on the
top ranking in Table 2, where 555 Twitter publications were presented at different conferences but
they come under the umbrella of LNCS in the WoS database. Same is the case with Lecture Notes
in Artificial Intelligence (LNAI) which is a subseries of LNCS under Springer database, but it is
considered as a separate source in the WoS database indicating 149 Twitter publications in Table 2.
These sources are unlike other sources, which are mostly single sources in top 10 most influential
journals.
In this section, we applied total publications, citations, citation links, bibliographic coupling links
and co-authorship links to explore the prominence of countries in Twitter research publications (see
Table 3). When one item (country’s publications in this case) cites other items, a citation link is
established between two items. But when two items cite same document, a bibliographic coupling
link is established between two items. Co-author analysis measures collaboration among authors
of different countries working on same document (Zupic & Čater, 2015). Total number of citations
measures influence of underlying country in Table 3.
According to data retrieved from the WoS, we found 109 countries contribution in 11006
publications. We chose type of analysis as “co-authorship,” “citation,” and “bibliographic coupling”
one by one and unit of analysis as “countries” in VOSviewer. Furthermore, we chose 1 minimum
Table 1. The top 20 Twitter research area categories
Sr. No. Research Areas Records
1 Computer science 5631
2 Engineering 2162
3 Communication 1028
4 Information science & library science 736
5 Telecommunications 663
6 Business economics 498
7 Science technology other topics 411
8 Psychology 400
9 Social sciences other topics 319
10 Education educational research 285
11 Health care sciences services 274
12 Public environmental occupational health 252
13 Sociology 245
14 Medical informatics 225
15 Government law 189
16 Operations research management science 185
17 Geography 136
18 Environmental sciences ecology 131
19 General internal medicine 124
20 Mathematics 124
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number of documents and 1 minimum number of citations of a country to get maximum number
of links generated among countries. Finally, we selected top 10 countries with highest number of
publications as shown in Table 3. The most dominant country was USA, owning 3586 total publications
(TP) and 73 co-author links. This indicates USA authors collaboration with 73 out of 108 countries
to work on Twitter research, while 105 bibliographic coupling links let researchers knew, there were
105 other countries those cited the same references in their Twitter publications as cited by USA.
46268 citations represent the number of times USA articles were cited by other publications. China
was the runner up with 892 TP and 4430 citations. England proportion of citations was the second
highest (9909) with 816 TP. This made authors to know, despite having more TP in Twitter research;
China got fewer citations as compared to England.
Two main clusters have been emerged during bibliometric network analysis through bibliographic
coupling in country analysis (see Figure 2). In this bibliometric mapping, each vertex represents
a country, while vertex size relates to weight (publications) in the visualization network and each
vertex colour represents a specific cluster. USA, Japan, India, China, Saudi Arabia, Turkey, Malaysia
and Pakistan were seen in the first cluster (red color). On the other hand, Norway, England, Finland,
Denmark, Belgium and Netherlands were seen together in the second cluster (green color). This
bibliographic coupling network indicates that the Twitter research papers published in North America
and Asia cited similar research articles in their research and fall in the first cluster while European
countries cited same Twitter articles in their own research and form the second cluster.
According to Twitter data (11006 articles), there were total 5616 institutes around the globe those
published Twitter research articles. We conducted “citation analysis” of above institutes. We chose
type of analysis as “citation” and “bibliographic coupling” one by one, and unit of analysis as
“organizations” in VOSviewer. Furthermore, we chose 20 minimum number of citations and 1
document of an organization and consequently got 1116 most noteworthy institutes. The top 21
institutions were sorted according to their citation scores as shown in Table 4. The visualization
network of productive institutions is shown in Figure 3.
University of Illinois (99 TP) and University of Arizona State (90 TP) got the highest number
of publications. The most productive institutes with respect to citation links with other institutes
Table 2. Top 10 most influential journals in twitter study
Sr. No Source Titles TP % of 11006 SCIE/
SSCI IF
1 Lecture notes in computer science 555 5.043 CPCI -
2 Plos one 219 1.99 SCIE 2.76
3 Computers in human behavior 155 1.408 SSCI 3.53
4 Lecture notes in artificial intelligence 149 1.354 CPCI
5 Journal of medical internet research 123 1.118 SSCI 4.67
6 Information communication & society 114 1.036 SSCI 3.08
7 Procedia computer science 93 0.845 CPCI
8 New media & society 89 0.809 SSCI 4.18
9 Advances in intelligent systems and computing 81 0.736 CPCI -
10 Communications in computer and information science 81 0.736 CPCI
TP= Total Publications; CPCI=Conference Proceeding Citation Index; SCIE/SSCI= Science Citation Index Expended/ Social Science Citation Index; IF=
Impact Factor
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(LWOI) were University of Toronto (526 LWOI), followed by the Indiana University (461 LWOI).
The total number of publications of Arizona State University and University of Maryland were similar
(90 and 87 respectively), but their citation score had a great difference (1424 and 2464 respectively)
which indicated the significance and importance of University of Maryland in Twitter research filed.
The most authoritative institute with respect to citations was ESCP EUROPE with 3300 citations
with 3 TP. This highest citation score was due to the Kaplan & Haenlein (2010) paper that received
Table 3. Top 10 most influential countries in twitter study
Sr. No. COUNTRY TP Citations BC Links Citation Links CA Links
1 Usa 3586 46268 105 85 73
2 Peoples Rep. of China 892 4430 103 64 39
3 England 816 9909 105 76 62
4 India 612 856 102 61 41
5 Australia 503 4132 102 65 45
6 Canada 485 6459 103 67 44
7 Spain 416 3034 103 66 48
8 Germany 397 4040 103 62 45
9 Japan 397 1519 100 47 25
10 Italy 333 2013 102 63 43
BC Links=Bibliographic Coupling links; CA Links=Co-authorship links
Figure 2. Most influential countries in Twitter research
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3170 citation score. Despite of having top QS World University Rankings (2018) around the globe,
Harvard University (3rd in ranking) and Oxford University (6th in ranking) published only 52 TP
(1169 citations) and 55 TP (909 citations) respectively. University of Michigan (986 BC-Links)
was the most prestigious one with highest bibliographic coupling links. This enormous number of
bibliographic coupling links indicated 986 other universities cited same articles in their research as
cited by University of Michigan. Furthermore, University of Indiana (984 BC-Links) was second
highest and University of Illinois (970 BC-Links) was third.
In Figure 3, five major clusters have been emerged through the bibliographic coupling network.
It can been seen that there is significant contribution of American universities in three clusters (blue,
yellow and purple colors). For example, Illinois University and Penn state University (yellow cluster)
publications have cited similar Twitter publications therefore they fall in this cluster. Whereas, Arizona
State University and Maryland University publications have cited similar Twitter publications (blue
color). In red cluster, Tsinghua University and the Chinese Academy of Science publications have cited
similar Twitter publications, therefore they fall in the first cluster. A mixture of different countries
institutions has been seen in green cluster as they cited similar Twitter publications, e.g., Melbourne
University (Australia), Toronto University (Canada) and Michigan University (USA).
Table 4. Top 21 most influential institutes in Twitter study
Sr. No Institutions TP Citations BC Links Citations Link
1 Escp Europe 3 3300 379 256
2 Indiana Univ. 73 2577 984 461
3 Univ. Maryland 87 2464 944 436
4 Penn State Univ. 69 2339 944 394
5 Univ. Toronto 68 2102 961 526
6 Univ. Illinois 99 1707 970 378
7 Univ. Manchester 32 1596 895 359
8 Nyu 64 1500 951 439
9 Arizona State Univ. 90 1424 958 304
10 Microsoft Res. 36 1341 859 154
11 Claremont Grad Univ. 15 1299 411 200
12 Wolverhampton Univ. 16 1213 871 294
13 Harvard Univ. 52 1169 943 358
14 Simon Fraser Univ. 34 1127 856 179
15 Univ. Texas Austin 62 1111 933 268
16 Univ. Michigan 66 1109 986 416
17 Northeastern Univ. 49 1035 901 263
18 Univ. Hlth Network 9 977 461 331
19 Univ. N. Carolina 47 964 941 333
20 Suny Buffalo 37 913 905 255
21 Univ. Oxford 55 909 887 240
LWOI = link with other institutes; TP= total publications; UNIV = University
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In this analysis, we explored the underlying research themes related to Twitter through the co-
occurrence of keywords. These keywords were recognized in the title, keywords and abstract of
the retrieved publications related to Twitter. VOSviewer was used to conduct the “co-occurrence
analysis” of key terms in Twitter publications. We chose type of analysis as “co-occurrence” and
unit of analysis as “All keywords” in VOSviewer. Furthermore, we chose 50 minimum number of
occurrences of a key term. The key term network visualization map was constructed based on the
co-occurrence frequencies of top 111 key terms out of total 18414 retrieved key terms. These 111
key terms provides a network of themes (Figure 4) and each theme represents the specific research
field (Zupic & Čater, 2015). The vertices size represents the frequency of occurrence of a specific
key term. The bigger the vertex size, greater the number of occurrence of a key term. Co-occurrence
analysis constructed 5 major clusters as shown in Figure 4 and listed in Table 5.
4.5.1 Cluster 1 (Red): Twitter as Micro Blogging Platform
for Sentiment Analysis and Event Detection
Twitter, social network, micro blogging, sentiment analysis and user generated content were prominent
keywords in this cluster. This let us knew that the most publications were based on sentiment analysis
and Twitter has been prominent in information retrieval and text mining in recent technological era.
User-generated content challenges researchers to mine treasure from the massive message streams.
This finding overlapped the findings of Kapoor et al., (2017). This cluster depicted Twitter role in
sentiment analysis during natural disaster (Mondalet al., 2018; Utz et al., 2013; Yuan & Liu, 2018)
and customer feedback (Hao et al., 2013). This cluster also explored Twitter advantage in real-time
information awareness. Prior Twitter publications suggested diverse optimize algorithms to ensure
rapid information sharing through event happening (Jarwar et al., 2017; Zhou & Chen, 2014), real time
Figure 3. Most influential institutes in Twitter research
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event detection (Escamilla et al., 2016; Hasan et al., 2018), event-based stock prediction (Makrehchi
et al., 2013), modelling propagation of public opinions on Twitter (Fang et al., 2017) and earthquake
reporting (Sakaki et al., 2013).
4.5.2 Cluster 2 (Green): Twitter as E-Health and Education
Facebook, YouTube, education, social networking sites, care, health and age were prominent keywords
in this cluster. This indicated that Twitter research had been conducted in collaboration with other
social networking sites such as YouTube and Facebook in education (Forgie et al., 2013; Kassens-Noor,
2012) and health. We also found Twitter role in antibiotics (Scanfeld et al., 2010), drug advertising
(Liang & Mackey, 2011), mothers communities for baby health, spreading diabetes related health
information (Harris et al., 2013) and finding healthcare issues (Influenza) from Twitter (Lali et al.,
2017). Furthermore, we found nurse education (Bristol, 2010) and higher education (Bahner et al.,
2012) from the perspective of Twitter. Social networking sites ubiquity in health and education has
reshaped today’s health and education by giving birth to E-Health (Norman & Yip, 2012) and E-
Education (Veletsianos, 2012).
4.5.3 Cluster 3 (Blue): Twitter as Media Platform for Politics and Journalism Community
In this cluster, election, politics, coverage, news, tweet and blogs were prominent keywords. It clearly
indicated explicit role of Twitter usage among journalist community and news broadcasting agencies
to keep up to date their followers by sending breaking news through tweets (Deprez & Van Leuven,
2017). The use of the Twitter by the political parties during election campaigns and forecasting the
election results (Small, 2011; Tumasjan et al., 2010) has been very common in today’s digital era.
Figure 4. Most occurred key terms in Twitter research
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99
4.5.4 Cluster 4 (Purple): Twitter as a Medium for Crisis
and Emergency Risk Communication
This is relatively small cluster as compared with other above clusters but its significance cannot be
declined because of Twitter role in crisis and emergency risk communication (CERC). CERC provides
relevant and quick information within a narrow time span for making the best decisions about safety
and well-being of the people (Reynolds & Seeger, 2005). This cluster indicated affective role of
Twitter in disaster management especially in communication crisis (Utz et al., 2013), California’s
drought risk management (Tang et al., 2015), disaster awareness (Rogstadius et al., 2013), public
trauma (Woo et al., 2015), disaster relief (Gao et al., 2011) and hurricane disaster (Yuan & Liu, 2018).
Furthermore, significant role of Twitter is found in risk reduction and crisis management (Alexander,
2014), tracking suicide risk (Jashinsky et al., 2014), bank risk contagion (Cerchiello et al., 2017) and
human papillomavirus risk (Lama et al., 2018).
4.5.5 Cluster 5 (Yellow): Networking Through Twitter
Communities, network, innovation and knowledge were prominent keywords in this cluster. This theme
illustrated that Twitter serves as virtual community platform in which users can come together to
share information to interact with one another. For example, teaching communities (Macià & García,
2016), weight loss communities (Tiggemann et al., 2018), harnessing semantic features for virtual
communities (Kalloubi et al., 2017) and disease specific communities (Pemmaraju et al., 2017).
Furthermore, bundle of social networks also exist in Twitter platform e.g. Twitter for educational
networking (Chan & Leung, 2018) and young adults in political campaign networking (Loader et
al., 2016).
Table 5. Themes in Twitter publications
Cluster
name
Theme Sub-theme Examples from prior studies
1-Red
Cluster
Twitter as micro
blogging platform
for sentiment
analysis and event
detection.
Natural disaster, customer feedback,
entertainment domain, event-based
stock prediction, event detection
and earthquake reporting, event
detection bibliometric.
Mondal et al., 2018; Utz et al., 2013; Yuan &
Liu, 2018, Hao et al., 2013; García-Silva et
al., 2013; Li et al., 2012; Sakaki et al., 2013;
Zhou & Chen, 2014; Hasan et al., 2018;
Makrehchi et al., 2013
2-Green
Cluster
Twitter as E-health
and E-education
Higher education, medical
education
Hawn, 2009; Vance, 2009; Laranjo et al.,
2015
3-Blue
Cluster
Twitter as media
platform for politics
and journalism
community
Political campaigns, news coverage Deprez & Van Leuven, 2017; Tumasjan et
al., 2010; Small, 2011
4-Purple
Cluster
Twitter as a medium
for addressing
information,
disaster and risk
Communication crisis, drought risk
management, disaster awareness,
Public Trauma relief, hurricane
disaster, risk reduction and crisis
management, tracking suicide
Utz et al., 2013; Rogstadius et al., 2013; Woo
et al., 2015; Gao et al., 2011; Yuan & Liu,
2018; Alexander, 2014
5-Yellow
Cluster
Networking through
Twitter.
Information sharing in health,
education, young adults in political
campaigns networking.
Loader et al., 2016; Macià & García, 2016;
Pemmaraju et al., 2017;
Tiggemann et al., 2018
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Some prominent findings of this study are identified here as key highlights:
• The annual growth rate of Twitter publications increased from 202 in 2010 to 1381 in 2014
depicting exponential development of Twitter research.
• The most common categories were Computer Science, Engineering, Communication, Information
Science and Telecommunications.
• The most dominant country was the USA; owing 3586 TP. China was the runner up with 892
TP. England proportion of citations was the second highest (9909) with 816 TP.
• University of Illinois and University of Arizona State have the highest number of publications.
• The most productive institutes with respect to citation LWOI were University of Toronto followed
by Indiana University.
• Kaplan and Haenlein (2010) paper got 3170 citations in Twitter research field and made ESCP
EUROPE most authoritative institute.
• Co-occurrence of keywords in Twitter yielded five major themes in twitter publications.
a) Twitter as micro blogging platform for sentiment analysis and event detection
b) Twitter as E-health and education
c) Twitter as a medium for politics and journalism community
d) Twitter as a medium for crisis and emergency risk communication
e) Networking through Twitter
According to the general literature, Twitter has been a practical tool that has allowed researchers
to gather new ideas and public opinion. This bibliometric study provides contribution of the most
productive journal, categories, countries, organizations and key terms evolution of underlying concepts.
This study provides new research streams to researchers, to be narrowed down by using Twitter. Event
detection, sentiment analysis, crisis and risk communication, crisis and risk management, education
and health are hotspot in Twitter research.
Despite of this, some implications for society are as follows: (1) Event detection has a lot more
room to suggest new techniques to optimize event detection on Twitter so information should be
easily reachable to folks. (2) Twitter role in health is also undeniable. One hospital (Anne Arundel
Medical Center) in Maryland is primarily utilizing Twitter to push hospital news, to steer people to the
hospital website and toward press releases (Terry, 2009). Our study provides implication for hospitals
to use Twitter platform not only for marketing purpose but also to provide drug information as well
as general health care facilities to their patients. (3) Twitter role has been found in higher education,
nurse education and medical education. This study opens new doors for research community to come
up with some new models to use Twitter online communities in other service sectors, particularly
beauty salons, consulting companies, banks and airlines companies.
The work done in this study provides an overview of the Twitter research dynamics in a glance. The
results suggested that academic knowledge related to Twitter has flourished from last decade and
still hotspot for new academic research. Thematic analysis revealed that Twitter is no longer just a
platform for socialization, but is being acknowledged as a source of user generated data to have a
collective opinion (sentiment analysis). Twitter is also playing a vital role in re-engineering health,
education and other diverse disciplines by using social networking and online communities. Political
and journalist communities are the top beneficiary of Twitter platform. Twitter role in crisis and
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emergency risk communication is remarkable. However, still there is room available to investigate
the role of Twitter in society as well as other fields.
Despite of contribution of this study in Twitter research, it has some limitations: (1) the analysis
is based on the data provided by WoS, which is considered as the most authenticated source of data.
However, still we could not claim that data is free of errors but accurate enough to support this
bibliometric study. Furthermore, the analytical framework suggested in this study can be expanded to
databases other than SCI/SSCI and other time periods. (2) This study did not conduct most influential
authors in Twitter research domain, so future studies are needed to explore this domain. (3) This is
the first bibliometric analysis, where citation links, bibliographic coupling and co-authorship links
has been explicitly used to explore underlying significance of countries and institutions in Twitter
research. Other researchers may use these bibliometric indicators in different research fields to explore
underlying research areas. However, this study provides enormous information of Twitter research at
a glance and open new avenues for other researchers to conduct research on twitter.
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Al-Barakati, A., & Daud, A. (2018). Venue-Influence Language Models for Expert Finding in Bibliometric
Networks. International Journal on Semantic Web and Information Systems, 14(3), 184–201. doi:10.4018/
IJSWIS.2018070109
Alexander, D. E. (2014). Social Media in Disaster Risk Reduction and Crisis Management. Science and
Engineering Ethics, 20(3), 717–733. doi:10.1007/s11948-013-9502-z PMID:24306994
Almgren, S. M., & Olsson, T. (2015). Let’s Get Them Involved… to Some Extent: Analyzing Online News
Participation. Social Media and Society, 1(2), 1–11. doi:10.1177/2056305115621934
Alotaibi, N. M., Guha, D., Fallah, A., Aldakkan, A., Nassiri, F., Badhiwala, J. H., & Lozano, A. M. et al.
(2016). Social Media Metrics and Bibliometric Profiles of Neurosurgical Departments and Journals: Is There a
Relationship? World Neurosurgery, 90, 574–579. doi:10.1016/j.wneu.2016.01.087 PMID:26855310
Appio, F. P., Martini, A., Massa, S., & Testa, S. (2016). Unveiling the Intellectual Origins of Social Media-
based Innovation: Insights from a Bibliometric Approach. Scientometrics, 108(1), 355–388. doi:10.1007/
s11192-016-1955-9
Bahner, D. P., Adkins, E., Patel, N., Donley, C., Nagel, R., & Kman, N. E. (2012). How We use Social Media
to Supplement a Novel Curriculum in Medical Education. Medical Teacher, 34(6), 439–444. doi:10.3109/014
2159X.2012.668245 PMID:22449268
Benetoli, A., Chen, T. F., & Aslani, P. (2015). The use of Social Media in Pharmacy Practice and Education.
Research in Social & Administrative Pharmacy, 11(1), 1–46. doi:10.1016/j.sapharm.2014.04.002 PMID:24814268
Bharti, S. K., Pradhan, R., Babu, K. S., & Jena, S. K. (2017). Sarcastic Sentiment Detection Based on Types of
Sarcasm Occurring in Twitter Data. International Journal on Semantic Web and Information Systems, 13(4),
89–108. doi:10.4018/IJSWIS.2017100105
Bonilla, C. A., Merigó, J. M., & Torres-Abad, C. (2015). Economics in Latin America: A Bibliometric Analysis.
Scientometrics, 105(2), 1239–1252. doi:10.1007/s11192-015-1747-7
Boyd, D. M., & Ellison, N. B. (2007). Social network Sites: Definition, History, and Scholarship. Journal of
Computer-Mediated Communication, 13(1), 210–230. doi:10.1111/j.1083-6101.2007.00393.x
Bristol, T. J. (2010). Twitter: Consider the Possibilities for Continuing Nursing Education. Journal of Continuing
Education in Nursing, 41(5), 199–200. doi:10.3928/00220124-20100423-09 PMID:20481418
Cerchiello, P., Giudici, P., & Nicola, G. (2017). Twitter Data Models for Bank Risk Contagion. Neurocomputing,
264, 50–56. doi:10.1016/j.neucom.2016.10.101
Chamberlin, L., & Lehmann, K. (2011). Twitter in Higher Education. Cutting-Edge Technologies in Higher
Education, 1(2), 375–391. doi:10.1108/S2044-9968(2011)0000001021
Chan, W. S. Y., & Leung, A. Y. M. (2018, March). Use of Social Network Sites for Communication among
Health Professionals: Systematic Review. Journal of Medical Internet Research, 20(3), e117. doi:10.2196/
jmir.8382 PMID:29592845
Chen, X., Wang, S., Tang, Y., & Hao, T. (2019). A Bibliometric Analysis of Event Detection in Social Media.
Online Information Review, 43(1), 29–52. doi:10.1108/OIR-03-2018-0068
Coupé, T. (2003). Revealed Performances: Worldwide Rankings of Economists and Economics Departments,
1990-2000. Journal of the European Economic Association, 1(6), 1309–1345. doi:10.1162/154247603322752557
Davidson, P. M., Newton, P. J., Ferguson, C., Daly, J., Elliott, D., Homer, C., & Jackson, D. etal. (2014). Rating
and Ranking the Role of Bibliometrics and Webometrics in Nursing and Midwifery. The Scientific World Journal,
2014, 1–6. doi:10.1155/2014/135812 PMID:24550691
Deprez, A., & Van Leuven, S. (2017). About Pseudo Quarrels and Trustworthiness. Journalism Studies, 19(9),
1257–1274. doi:10.1080/1461670X.2016.1266910
Di Fatta, D., & Yolles, M. (2017). Modelling Multiples Identity Types Through Agency: Part 3 – Mindsets and
the Trump Election. Kybernetes, 47(4), 638–655. doi:10.1108/K-03-2017-0112
Volume 16 • Issue 3 • July-September 2020
103
Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric Cartography of Information Retrieval Research
by using Co-word Analysis. Information Processing & Management, 37(6), 817–842. doi:10.1016/S0306-
4573(00)00051-0
Escamilla, I., Torres-Ruiz, M., Moreno-Ibarra, M., Quintero, R., Guzmán, G., & Luna-Soto, V. (2016). Geocoding
Tweets Approach Based on Conceptual Representations in the Context of the Knowledge Society. International
Journal on Semantic Web and Information Systems, 12(1), 44–61. doi:10.4018/IJSWIS.2016010103
Fang, Y., Chen, X., Song, Z., Wang, T., & Cao, Y. (2017). Modelling Propagation of Public Opinions on
Microblogging Big Data Using Sentiment Analysis and Compartmental Models. International Journal on
Semantic Web and Information Systems, 13(1), 11–27. doi:10.4018/IJSWIS.2017010102
Forgie, S. E., Duff, J. P., & Ross, S. (2013). Twelve tips for using Twitter as a learning tool in medical education.
Medical Teacher, 33(1), 8–14. doi:10.3109/0142159X.2012.746448 PMID:23259608
Gan, C., & Wang, W. (2014). A Bibliometric Analysis of Social Media Research from the Perspective of
Library and Information Science. In Digital Services and Information Intelligence (pp. 23-32). Academic Press.
doi:10.1007/978-3-662-45526-5_3
Gao, H., Wang, X., Barbier, G., & Liu, H. (2011). Promoting Coordination for Disaster Relief – From
Crowdsourcing to Coordination. In Proceedings of International Conference on Social Computing, Behavioral-
Cultural Modeling, and Prediction (pp. 197–204). Academic Press. doi:10.1007/978-3-642-19656-0_29
García-Silva, A., Rodríguez-Doncel, V., & Corch, O. (2013). Semantic Characterization of Tweets Using Topic
Models: A Use Case in the Entertainment Domain. International Journal on Semantic Web and Information
Systems, 9(3), 1–13. doi:10.4018/ijswis.2013070101
Garfield, E. (1955). Citation Indexes for Science: A New Dimension in Documentation through Association of
Ideas. Science, 122(3159), 108–111. doi:10.1126/science.122.3159.108 PMID:14385826
Grover, P., Kar, A. K., & Davies, G. (2018). Technology Enabled Health – Insights from Twitter Analytics with
a Socio-Technical Perspective. International Journal of Information Management, 43, 85–97. doi:10.1016/j.
ijinfomgt.2018.07.003
Guerrero-Solé, F. (2018). Interactive Behavior in Political Discussions on Twitter: Politicians, Media, and
Citizens’ Patterns of Interaction in the 2015 and 2016 Electoral Campaigns in Spain. Social Media + Society,
4(4), 1–16. 10.1177/2056305118808776
Hao, M. C., Rohrdantz, C., Janetzko, H., Keim, D. A., Dayal, U., Haug, L., & Stoffel, F. etal. (2013). Visual
Sentiment Analysis of Customer Feedback Streams using Geo-Temporal Term Associations. Information
Visualization, 12(3–4), 273–290. doi:10.1177/1473871613481691
Harris, J. K., Mueller, N. L., Snider, D., & Haire-Joshu, D. (2013). Local Health Department Use of Twitter
to Disseminate Diabetes Information, United States. Preventing Chronic Disease, 10, 120215. doi:10.5888/
pcd10.120215 PMID:23639765
Hasan, M., Orgun, M. A., & Schwitter, R. (2018). A Survey on Real-time Event Detection from the Twitter Data
Stream. Journal of Information Science, 44(4), 443–463. doi:10.1177/0165551517698564
Hawn, C. (2009). Take Two Aspirin And Tweet Me In The Morning: How Twitter, Facebook, And Other Social
Media are Reshaping Health Care. Health Affairs, 28(2), 361–368. doi:10.1377/hlthaff.28.2.361 PMID:19275991
Hopke, J. E. (2015). Hashtagging Politics: Transnational Anti-Fracking Movement Twitter Practices. Social
Media + Society, 1(2), 1–12. 10.1177/2056305115605521
Hull, K., & Dodd, J. E. (2017). Faculty use of Twitter in Higher Education Teaching. Journal of Applied Research
in Higher Education, 9(1), 91–104. doi:10.1108/JARHE-05-2015-0038
IPCC. (2013). Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press. doi:10.1017/CBO9781107415324.004
Jacobson, J., & Mascaro, C. (2016). Movember: Twitter Conversations of a Hairy Social Movement. Social
Media + Society, 2(2). doi:10.1177/2056305116637103
Volume 16 • Issue 3 • July-September 2020
104
Jarwar, M. A., Abbasi, R. A., Mushtaq, M., Maqbool, O., Aljohani, N. R., Daud, A., & Chong, I. etal. (2017).
CommuniMents: A Framework for Detecting Community Based Sentiments for Events. International Journal
on Semantic Web and Information Systems, 13(2), 87–108. doi:10.4018/IJSWIS.2017040106
Jashinsky, J., Burton, S. H., Hanson, C. L., West, J., Giraud-Carrier, C., Barnes, M. D., & Argyle, T. (2014).
Tracking Suicide Risk Factors Through Twitter in the US. Crisis, 35(1), 51–59. doi:10.1027/0227-5910/a000234
PMID:24121153
Kalloubi, F., Nfaoui, E. H., & El Beqqali, O. (2017). Harnessing Semantic Features for Large-Scale Content-
Based Hashtag Recommendations on Microblogging Platforms. International Journal on Semantic Web and
Information Systems, 13(1), 63–81. doi:10.4018/IJSWIS.2017010105
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social
Media. Business Horizons, 53(1), 59–68. doi:10.1016/j.bushor.2009.09.003
Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2017). Advances in Social
Media Research: Past, Present and Future. Information Systems Frontiers, 20(3), 531–558. doi:10.1007/s10796-
017-9810-y
Kassens-Noor, E. (2012). Twitter as a Teaching Practice to Enhance Active and Informal Learning in
Higher Education: The Case of Sustainable Tweets. Active Learning in Higher Education, 13(1), 9–12.
doi:10.1177/1469787411429190
Kokol, P., Saranto, K., & Blažun Vošner, H. (2018). eHealth and Health Informatics Competences. Kybernetes,
47(5), 1018–1030. doi:10.1108/K-09-2017-0338
Kwon, A. R., & Lee, K. S. (2013). Opinion Bias Detection with Social Preference Learning in Social Data.
International Journal on Semantic Web and Information Systems, 9(3), 57–76. doi:10.4018/ijswis.2013070104
Lali, M. I. U., Mustafa, R. U., Saleem, K., Nawaz, M. S., Zia, T., & Shahzad, B. (2017). Finding Healthcare Issues
with Search Engine Queries and Social Network Data. International Journal on Semantic Web and Information
Systems, 13(1), 48–62. doi:10.4018/IJSWIS.2017010104
Lama, Y., Chen, T., Dredze, M., Jamison, A., Quinn, S. C., & Broniatowski, D. A. (2018). Discordance between
Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United
States: Mixed-methods Analysis. Journal of Medical Internet Research, 20(9), e10244. doi:10.2196/10244
PMID:30217792
Laranjo, L., Arguel, A., Neves, A. L., Gallagher, A. M., Kaplan, R., Mortimer, N., & Lau, A. Y. S. etal. (2015).
The Influence of Social Networking Sites on Health Behavior Change: A Systematic Review and Meta-analysis.
Journal of the American Medical Informatics Association, 22(1), 243–256. doi:10.1136/amiajnl-2014-002841
PMID:25005606
Lee, C. H., Yang, H. C., Chien, T. F., & Wen, W. S. (2011). A Novel Approach for Event Detection by Mining
Spatio-temporal Information on Microblogs. In Proceedings of International Conference on Advances in Social
Networks Analysis and Mining (pp. 254–259). Academic Press. doi:10.1109/ASONAM.2011.74
Li, Q., Wei, W., Xiong, N., Feng, D., Ye, X., & Jiang, Y. (2017). Social Media Research, Human Behavior, and
Sustainable Society. Sustainability, 9(3), 384. doi:10.3390/su9030384
Li, R., Lei, K. H., Khadiwala, R., & Chang, K. C. C. (2012). TEDAS: A Twitter-based Event Detection and
Analysis System. In Proceedings of International Conference on Data Engineering (pp. 1273–1276). Academic
Press. doi:10.1109/ICDE.2012.125
Liang, B. A., & Mackey, T. K. (2011). Prevalence and Global Health Implications of Social Media in Direct-
to-Consumer Drug Advertising. Journal of Medical Internet Research, 13(3), e64. doi:10.2196/jmir.1775
PMID:21880574
Liang, T. P., & Liu, Y. H. (2018). Research Landscape of Business Intelligence and Big Data Analytics: A
Bibliometrics Study. Expert Systems with Applications, 111, 2–10. doi:10.1016/j.eswa.2018.05.018
Llanos-Herrera, G. R., & Merigo, J. M. (2019). Overview of Brand Personality Research with Bibliometric
Indicators. Kybernetes, 48(3), 546–569. doi:10.1108/K-02-2018-0051
Volume 16 • Issue 3 • July-September 2020
105
Loader, B. D., Vromen, A., & Xenos, M. A. (2016). Performing for the Young Networked Citizen? Celebrity
Politics, Social Networking and the Political Engagement of Young People. Media Culture & Society, 38(3),
400–419. doi:10.1177/0163443715608261
Macià, M., & García, I. (2016). Informal Online Communities and Networks as a Source of Teacher Professional
Development: A Review. Teaching and Teacher Education, 55, 291–307. doi:10.1016/j.tate.2016.01.021
Makrehchi, M., Shah, S., & Liao, W. (2013). Stock Prediction using Event-based Sentiment Analysis. In
Proceedings of 2013 International Conference on Web Intelligence (pp. 337–342). doi:10.1109/WI-IAT.2013.48
McGregor, S. C., & Mourão, R. R. (2016). Talking Politics on Twitter: Gender, Elections, and Social Networks.
Social Media + Society, 2(3), 1–12. doi:10.1177/2056305116664218
Mondal, T., Pramanik, P., Bhattacharya, I., Boral, N., & Ghosh, S. (2018). Analysis and Early Detection of Rumors
in a Post Disaster Scenario. Information Systems Frontiers, 20(5), 961–979. doi:10.1007/s10796-018-9837-8
Muller, A. M., Maher, C. A., Vandelanotte, C., Hingle, M., Middelweerd, A., Lopez, M. L., & Wark, P. A. etal.
(2018). Physical Activity, Sedentary Behavior, and Diet-Related eHealth and mHealth Research: Bibliometric
Analysis. Journal of Medical Internet Research, 20(4), e122. doi:10.2196/jmir.8954 PMID:29669703
Mustafa, R. U., Nawaz, M. S., Lali, M. I., Zia, T., & Mehmood, W. (2017). Predicting The Cricket Match Outcome
Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods. Malaysian
Journal of Computer Science, 30(1), 63–76.
Nawaz, M. S., Bilal, M., Lali, M. I., Mustafa, U., Waqar, A., & Jajja, S. (2017). Effectiveness of Social Media
Data in Healthcare Communication. Journal of Medical Imaging and Health Informatics, 7(6), 1365–1371.
Noor, S., Guo, Y., Shah, S. H. H., Nawaz, M. S., & Butt, A. S. (2020). Bibliometric Analysis of Social Media
as a Platform for Knowledge Management. International Journal of Knowledge Management, 16(3).
Norman, C. D., & Yip, A. L. (2012). eHealth Promotion and Social Innovation with Youth: Using Social and
Visual Media to Engage Diverse Communities. In Quintana, Villalobos, & May (Eds.), Advancing Cancer
Education and Healthy Living in our Communities: Putting Visions and Innovations into Action (pp. 54–70).
IOS Press. doi:10.3233/978-1-61499-088-8-54
Pan, X., Yan, E., Cui, M., & Hua, W. (2018). Examining the usage, citation, and diffusion patterns of bibliometric
mapping software: A Comparative Study of Three tools. Journal of Informetrics, 12(2), 481–493. doi:10.1016/j.
joi.2018.03.005
Pemmaraju, N., Thompson, M. A., & Qazilbash, M. (2017). Disease-specific Hashtags and the Creation of Twitter
Medical Communities in Hematology and Oncology. Seminars in Hematology, 54(4), 189–192. doi:10.1053/j.
seminhematol.2017.08.004 PMID:29153079
Perianes-Rodriguez, A., Waltman, L., & van Eck, N. J. (2016). Constructing Bibliometric Networks: A
Comparison Between Full and Fractional Counting. Journal of Informetrics, 10(4), 1178–1195. doi:10.1016/j.
joi.2016.10.006
Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly Influence in the Field
of Management: A Bibliometric Analysis of the Determinants of University and Author Impact in the Management
Literature in the Past Quarter Century. Journal of Management, 34(4), 641–720. doi:10.1177/0149206308319533
Pritchard, A. (1969). Statistical Bibliography or Bibliometrics? The Journal of Documentation, 25(4), 348–349.
Reynolds, B., & Seeger, M. W. (2005). Crisis and Emergency Risk Communication as an Integrative Model.
Journal of Health Communication, 10(1), 43–55. doi:10.1080/10810730590904571 PMID:15764443
Rogstadius, J., Vukovic, M., Teixeira, C. A., Kostakos, V., Karapanos, E., & Laredo, J. A. (2013). CrisisTracker:
Crowdsourced Social Media Curation for Disaster Awareness. IBM Journal of Research and Development, 57(5),
4:1-4:13. doi:10.1147/jrd.2013.2260692
Sakaki, T., Okazaki, M., & Matsuo, Y. (2013). Earthquake Shakes Twitter Users: Real-time Event Detection
by Social Sensors. IEEE Transactions on Knowledge and Data Engineering, 25(4), 919–931. doi:10.1109/
TKDE.2012.29
Volume 16 • Issue 3 • July-September 2020
106
Scanfeld, D., Scanfeld, V., & Larson, E. L. (2010). Dissemination of Health Information through Social Networks:
Twitter and Antibiotics. American Journal of Infection Control, 38(3), 182–188. doi:10.1016/j.ajic.2009.11.004
PMID:20347636
Shah, S. H. H., Lei, S., Ali, M., Doronin, D., & Hussain, S. T. (2019). Prosumption: Bibliometric Analysis using
HistCite and VOSviewer. Kybernetes, 1–24. doi:10.1108/K-12-2018-0696
Shah, S. H. H., Lei, S., Noor, S., & Anjum, A. (2020). Research Synthesis and New Directions of Prosumption:
A bibliometric analysis. International Journal of Information and Management Sciences, 31(1), 79–98. https://
doi.org/ 31(1).000510.6186/IJIMS.202003
Shilbury, D. (2016). A Bibliometric Study of Citations to Sport Management and Marketing Journals. Journal
of Sport Management, 25(5), 423–444. doi:10.1123/jsm.25.5.423
Small, T. A. (2011). What the Hashtag?: A Content Analysis of Canadian Politics on Twitter. Information
Communication and Society, 14(6), 872–895. doi:10.1080/1369118X.2011.554572
Sotiriadis, M. D., & van Zyl, C. (2013). Electronic Word-of-mouth and Online Reviews in Tourism Services:
The use of Twitter by Tourists. Electronic Commerce Research, 33(1), 103–124. doi:10.1007/s10660-013-9108-1
Tang, M., Liao, H., Wan, Z., Herrera-Viedma, E., & Rosen, M. A. (2018). Ten Years of Sustainability (2009
to 2018): A Bibliometric Overview. Sustainability, 10(5), 1–21. doi:10.3390/su10051655 PMID:30607262
Tang, Z., Zhang, L., Xu, F., & Vo, H. (2015). Examining the Role of Social Media in California’s Drought Risk
Management in 2014. Natural Hazards, 79(1), 171–193. doi:10.1007/s11069-015-1835-2
Terry, M. (2009). Twittering Healthcare: Social Media and Medicine. Telemedicine Journal and e-Health, 15(6),
507–510. doi:10.1089/tmj.2009.9955 PMID:19659410
Tiggemann, M., Churches, O., Mitchell, L., & Brown, Z. (2018). Tweeting Weight Loss: A Comparison
of #thinspiration and #fitspiration Communities on Twitter. Body Image, 25, 133–138. doi:10.1016/j.
bodyim.2018.03.002 PMID:29567619
Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Election Forecasts With Twitter. Social
Science Computer Review, 29(4), 402–418. doi:10.1177/0894439310386557
Twitter Stats. (2019). Available at: https://www.omnicoreagency.com/twitter-statistics. Accessed on August
23, 2019.
Utz, S., Schultz, F., & Glocka, S. (2013). Crisis Communication Online: How Medium, Crisis Type and Emotions
Affected Public Reactions in the Fukushima Daiichi Nuclear Disaster. Public Relations Review, 39(1), 40–46.
doi:10.1016/j.pubrev.2012.09.010
van Eck, N. J., & Waltman, L. (2008). Appropriate Similarity Measures for Author Co‐citation Analysis. Journal
of the American Society for Information Science and Technology, 59(10), 1653–1661. doi:10.1002/asi.20872
van Eck, N. J., & Waltman, L. (2009). How to Normalize Cooccurrence Data? An Analysis of Some Well-
known Similarity Measures. Journal of the American Society for Information Science and Technology, 60(8),
1635–1651. doi:10.1002/asi.21075
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a Computer Program for Bibliometric
Mapping. Scientometrics, 84(2), 523–538. doi:10.1007/s11192-009-0146-3 PMID:20585380
van Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. In Measuring Scholarly Impact:
Measures and Practice (pp. 285-320). Springer. doi:10.1007/978-3-319-10377-8_13
Vance, K., Howe, W., & Dellavalle, R. P. (2009). Social Internet Sites as a Source of Public Health Information.
Dermatologic Clinics, 27(2), 133–136. doi:10.1016/j.det.2008.11.010 PMID:19254656
Veletsianos, G. (2012). Higher Education Scholars’ Participation and Practices on Twitter. Journal of Computer
Assisted Learning, 28(4), 336–349. doi:10.1111/j.1365-2729.2011.00449.x
Vošner, H. B., Kokol, P., Bobek, S., Železnik, D., & Završnik, J. (2016). A Bibliometric Retrospective of the
Journal Computers in Human Behavior (1991–2015). Computers in Human Behavior, 65, 46–58. doi:10.1016/j.
chb.2016.08.026
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Williams, J., Chinn, S. J., & Suleiman, J. (2014). The value of Twitter for sports fans. Journal of Direct. Data
and Digital Marketing Practice, 16(1), 36–50.
Woo, H., Cho, Y., Shim, E., Lee, K., & Song, G. (2015). Public Trauma after the Sewol Ferry Disaster: The
Role of Social Media in Understanding the Public Mood. International Journal of Environmental Research and
Public Health, 12(9), 10974–10983. doi:10.3390/ijerph120910974 PMID:26404349
Yuan, F., & Liu, R. (2018). Crowdsourcing for Forensic Disaster Investigations: Hurricane Harvey Case Study.
Natural Hazards, 93(3), 1529–1546. doi:10.1007/s11069-018-3366-0
Zhang, L., & Wang, J. (2018). Why Highly Cited Articles are not Highly Tweeted? A Biology Case. Scientometrics,
117(1), 495–509. doi:10.1007/s11192-018-2876-6
Zhou, X., & Chen, L. (2014). Event Detection over Twitter Social Media Streams. The VLDB Journal, 23(3),
381–400. doi:10.1007/s00778-013-0320-3
Zupic, I., & Čater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research
Methods, 18(3), 429–472. doi:10.1177/1094428114562629
Zyoud, S. H., Sweileh, W. M., Awang, R., & Al-Jabi, S. W. (2018). Global Trends in Research Related to Social
Media in Psychology: Mapping and Bibliometric Analysis. International Journal of Mental Health Systems,
12(1), 4. doi:10.1186/s13033-018-0182-6 PMID:29387147
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Table 6. Bibliometric studies in twitter and social media
Author / Title /Journal
1. Gan CM, Wang WJ
A Bibliometric Analysis of Social Media Research from the Perspective of Library and Information Science
DIGITAL SERVICES AND INFORMATION INTELLIGENCE. 2014; 445: 23-32
2. Haustein S, Peters I, Bar-Ilan J, Priem J, Shema H, et al.
Coverage and Adoption of Altmetrics Sources in the Bibliometric Community
SCIENTOMETRICS. 2014 NOV; 101 (2): 1145-1163
3. Hammarfelt B
Using Altmetrics for Assessing Research Impact in the Humanities
SCIENTOMETRICS. 2014 NOV; 101 (2): 1419-1430
4. Haustein S, Bowman TD, Costas R
When is an Article Actually Published? An Analysis of Online Availability, Publication, and Indexation Dates
PROCEEDINGS OF THE 15TH INTERNATIONAL SOCIETY OF SCIENTOMETRICS AND INFORMETRICS CONFERENCE. 2015; 1170-1179
5. Stewart B
Open to Influence: What Counts as Academic Influence in Scholarly Networked Twitter Participation
LEARNING MEDIA AND TECHNOLOGY. 2015 JUL 3; 40 (3): 287-309
6. Herrmannova D, Knoth P
Semantometrics Towards Fulltext-based Research Evaluation
2016 IEEE/ACM JOINT CONFERENCE ON DIGITAL LIBRARIES. 2016; 235-236
7. Biljecki F
A scientometric Analysis of SElected GIScience Journals
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE. 2016; 30 (7): 1302-1335
8. Timilsina M, Davis B, Taylor M, Hayes C
Towards Predicting Academic Impact from Mainstream News and Weblogs: A Heterogeneous Graph Based Approach
PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND
MINING ASONAM. 2016; 1388-1389
9. Nelhans G, Lorentzen DG
Twitter Conversation Patterns Related to Research Papers
INFORMATION RESEARCH. 2016 JUN; 21 (2): SM2
10. Alotaibi NM, Guha D, Fallah A, Aldakkan A, Nassiri F, et al.
Social Media Metrics and Bibliometric Profiles of Neurosurgical Departments and Journals: Is There a Relationship?
WORLD NEUROSURGERY. 2016 JUN; 90: 574-579
11. Scotti V, De Silvestri A, Scudeller L, Abele P, Topuz F, et al.
Novel Bibliometric Scores for Evaluating Research Quality and Output: A Correlation Study with Established Indexes
INTERNATIONAL JOURNAL OF BIOLOGICAL MARKERS. 2016 OCT-DEC; 31 (4): E451-E455
12. Mas-Bleda A, Thelwall M
Can Alternative Indicators Overcome Language Biases in Citation Counts? A Comparison of Spanish and UK Research
SCIENTOMETRICS. 2016 DEC; 109 (3): 2007-2030
13. Li Q, Wei WB, Xiong NA, Feng DC, Ye XY, et al.
Social Media Research, Human Behavior, and Sustainable Society
SUSTAINABILITY. 2017 MAR; 9 (3): 384
14. Sugimoto CR, Work S, Lariviere V, Haustein S
Scholarly Use of Social Media and Altmetrics: A Review of the Literature
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY. 2017 SEP; 68 (9): 2037-2062
15. Gul S, Shueb S, Shah R, Shah TA
Altmetrics for the Journals of Politics Correlating Altmetrics with Journal Metrics
IEEE 5TH INTERNATIONAL SYMPOSIUM ON EMERGING TRENDS AND TECHNOLOGIES IN LIBRARIES AND INFORMATION SERVICES.
2018; 397-401
16. Zyoud SH, Sweileh WM, Awang R, Al-Jabi SW
Global Trends in Research Related to Social Media in Psychology: Mapping and Bibliometric Analysis
INTERNATIONAL JOURNAL OF MENTAL HEALTH SYSTEMS. 2018 JAN 19; 12: 4
17. Ruano J, Aguilar-Luque M, Gomez-Garcia F, Mellado PA, Gay-Mimbrera J, et al.
The Differential Impact of Scientific Quality, Bibliometric Factors, and Social Media Activity on the Influence of Systematic Reviews and Meta-analyses
about Psoriasis
PLOS ONE. 2018 JAN 29; 13 (1): e0191124
18. Maricato JD, Vilan JL
The Potential for Altmetrics to Measure other Types of Impact in Scientific Production: Academic and Social Impact Dynamics in Social Media and
Networks
INFORMATION RESEARCH. 2018 MAR; 23 (1): 1-16
19. Lamb CT, Gilbert SL, Ford AT
Tweet Success? Scientific Communication Correlates with Increased Citations in Ecology and Conservation
PEERJ. 2018 APR 12; 6: e4564
20. Shu F, Lou W, Haustein S
Can Twitter Increase the Visibility of Chinese Publications?
SCIENTOMETRICS. 2018 JUL; 116 (1): 505-519
21. Ruano J, Aguilar-Luque M, Isla-Tejera B, Alcalde-Mellado P, Gay-Mimbrera J, et al.
Relationships between Abstract Features and Methodological Quality Explained Variations of Social Media Activity Derived from Systematic Reviews
about Psoriasis Interventions
JOURNAL OF CLINICAL EPIDEMIOLOGY. 2018 SEP; 101: 35-43
22. Zhang LW, Wang J
Why Highly Cited Articles are not Highly Tweeted? A Biology Case
SCIENTOMETRICS. 2018 OCT; 117 (1): 495-509
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Saleha Noor is a Ph.D. Scholar at School of Information Science and Engineering at East China Science and
Technology University, China. She received her M.S. degree in Computer Science from University of Sargodha,
Pakistan in 2016 and B.S. degree in Computer Science from University of Engineering and Technology Lahore,
Pakistan in 2011. Her current research interests are data mining, social media, and bibliometric analysis.
Yi Guo (过弋) received his Ph.D. degree in Computer Science from Heriot-Watt University, Edinburgh, UK in 2005.
He is currently a full professor at East China University of Science and Technology. His research focuses on text
mining, information extraction, knowledge discovery and business intelligence analysis. Now he is a member of
IEEE, CMI, IET, BCS and APMG-MSP/PRINCE2-Practitioner, and he is also a committee member of National
Engineering Laboratory for Big Data Distribution and Exchange Technologies. He is the corresponding author and
can be contacted at guoyi@ecust.edu.cn.
Hamad Shah is a Ph.D Scholar at Glorious Sun School of Business and Management, Donghua University,
Shanghai, China. His current research interests are social media, consumer behavior, and bibliometric analysis.
M. Saqib Nawaz completed his P.hD. from Peking University, Beijing, China in 2019. He received his M.S. degree in
Computer Science from University of Sargodha, Pakistan in 2014 and B.S. degree in Computer Systems Engineering
from University of Engineering and Technology, Peshawar, Pakistan in 2011. He is currently a postdoctoral fellow
at Harbin Institute of Technology, Shenzhen, China. His research interests include social network analytics, formal
methods, and software theory.
Atif Saleem Butt is an Assistant Professor in Logistics and Supply Chain Management at the American University
of Ras Al Khaimah, UAE. He received Ph.D. in Supply Chain Management from Monash University, Australia.
His area of interest includes behavioral dynamics in buyer-supplier relationships in the supply chain, and further
engaging in cross-disciplinary research. His work has appeared in journals of international repute including Journal
of Knowledge Management, International Journal of Knowledge Management, Management Research Review,
Benchmarking: an International Journal, Journal of Asia Business Studies and the International Public Management
Journal, among others.
