Beyond Disciplinary Boundaries: Mapping Educational Science in the Discourse on Social Media

Abstract

Social media has attracted considerable scholarly interest. Previous research has demonstrated the need for a more comprehensive overview of social media research across diverse disciplines. However, there is a lack of research that identifies the scope of social media integration across educational settings and how it relates to research in other academic disciplines. Harnessing the search terms of previous literature reviews, this study collected data on 80,267 articles from the Web of Science Core Collection database using search terms that were based on previous literature reviews. The data were analyzed using a combination of co-citation and bibliometric analyses via a mixed-methods approach. Our results show that there has been a constant increase in the number of publications concerned with social media, both as a transversal topic and within the educational sector. We are also able to show a range of topical domains in which the vast majority of research on social media is conducted. Our findings have practical implications for scholars and practitioners alike. Scholars can benefit from these types of analyses to identify authors and topic clusters that might otherwise have been unrecognized. Similarly, practitioners can benefit from this overview of the current “state-of-the-art” on social media.

Full text

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Teachers College Record Volume 121, 140303, 2019, 24 pages
Copyright © by Teachers College, Columbia University
0161-4681
Beyond Disciplinary Boundaries: Mapping
Educational Science in the Discourse on
Social Media
MARTIN REHM
University of Education, Weingarten
STEFANIA MANCA
Institute of Educational Technology, National Research Council of Italy
DIANA L. BRANDON
Charleston Southern University
CHRISTINE GREENHOW
Michigan State University
Social media has attracted considerable scholarly interest. Previous research has demonstrated
the need for a more comprehensive overview of social media research across diverse disciplines.
However, there is a lack of research that identifies the scope of social media integration across
educational settings and how it relates to research in other academic disciplines. Harnessing
the search terms of previous literature reviews, this study collected data on 80,267 articles
from the Web of Science Core Collection database using search terms that were based on
previous literature reviews. The data were analyzed using a combination of co-citation and
bibliometric analyses via a mixed-methods approach. Our results show that there has been
a constant increase in the number of publications concerned with social media, both as a
transversal topic and within the educational sector. We are also able to show a range of topical
domains in which the vast majority of research on social media is conducted. Our findings
have practical implications for scholars and practitioners alike. Scholars can benefit from
these types of analyses to identify authors and topic clusters that might otherwise have been
unrecognized. Similarly, practitioners can benefit from this overview of the current “state-of-
the-art” on social media.
Defining social media is a challenge (Obar & Wildman, 2015). However,
while the field is constantly evolving, and ever more (technological) affor-
dances are provided to interact with others (human and machine), schol-
ars have identified key aspects of social media. Obar and Wildman (2015)

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have provided a valuable contribution to this discussion, noting Web 2.0
applications, user-generated content, site-specific user profiles, and con-
necting individuals via online social networks. Moreover, boyd and Ellison
(2007) particularly focused on social network sites and provided a com-
prehensive overview of these sites, their main characteristics, and how
they might have changed over time, starting in 1997 with SixDegrees.com
and moving toward platforms such as Twitter and Facebook. Similarly,
Hampton (2016) specifically looked at the newest communication tech-
nologies and highlighted how different disciplines have dealt with the un-
derlying communication patterns and behavioral aspects of their users. In
his work, Hampton showed that the topic of social media has always been
a very multifaceted and interdisciplinary research domain.
While not conceived or designed for educational purposes, social media
have become increasingly popular for formal and informal learning in
many disciplinary areas. In this context, again, the applied definitions of
social media are rather diverse, encompassing a wide range of Internet-
based applications that allow the creation and exchange of user-gener-
ated content, such as social networking sites, blogs, and wikis (Kaplan &
Haenlein, 2010; Tess, 2013). The benefits of integrating social media into
learning and teaching within secondary and higher education have been
extensively studied since early 2000. Despite changes in dominant plat-
forms and brands (e.g., the demise of MySpace and the rise of Facebook),
scholars argue in article titles such as “Now [That] the Dust Has Settled”
that a critical balance of achievements and challenges is finally needed
(Selwyn & Sterling, 2016). In the early days of social media studies in edu-
cation, it seemed that social media were mostly investigated as a “killer
app” capable of being leveraged for significant returns in education, with
the majority of studies being on college students’ responses to social me-
dia adoption (Piotrowski, 2015). Today, a more critical stance is taken, and
scholars have questioned the suitability of commercial social media for
education (Frank & Torphy, 2019, this yearbook; Krutka et al., 2019, this
yearbook), as well as their adequacy to support argumentation, discussion
and knowledge construction (Friesen & Lowe, 2012; Greenhow, Menzer,
& Gibbins, 2015; Kirschner, 2015).
Moreover, investigations of faculty attitudes toward social media have
shown that, despite their positive outlook toward uses of social media
for personal sharing and professional development, many faculty are re-
luctant to teach with social media because of cultural and pedagogical
issues and lack of institutional support (see Manca & Ranieri, 2016a,
2016c). On the other hand, while educators’ resistance to instruction-
al uses of social media continues to be documented (Willems, Adachi,
Bussey, Doherty, & Huijser, 2018), investigation of research trends in

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instructional design and technology journals shows that social media
studies continue to attract scholarly interest worldwide (Bodily, Leary,
& West, 2019).
Despite educators’ concerns and critical arguments, social media have
indeed attracted considerable scholarly interest, and hundreds of studies
have been published on single platforms or on the combination of mul-
tiple social services. With an increasing number of studies concerned with
social media, literature reviews have also flourished in the social sciences
and in the field of education (Fox & Bird, 2017; Manca & Whitworth,
2018; Wilson, Gosling, & Graham, 2012; Zachos, Paraskevopoulou-Kollia,
& Anagnostopoulos, 2018). Social network sites especially have gained a
prominent position in the scholarly literature, and today they are the fo-
cal topic in thousands of articles in the major literature databases and
in literature reviews (Aydin, 2012; Greenhow & Askari, 2017; Manca
& Ranieri, 2013, 2016b). The educational value of social network sites
has been deeply studied in specialized journals and in thematic con-
ferences with a major focus on higher education (Zachos et al., 2018),
and the topic has been increasingly taken up in K–12 education and
related research as well (Daly, Liou, Del Fresno, Rehm, & Bjorklund,
2019, this yearbook; Daly, Supovitz, & Del Fresno, 2019, this yearbook;
Greenhow, Cho, Dennen, & Fishman, 2019, this yearbook; Greenhow,
Li, & Mai, in press; Greenhow & Robelia, 2009a, 2009b; Greenhow et
al., 2015; Greenhow, Burton, & Robelia, 2011; Krutka et al., 2019, this
yearbook). Relevant specific areas that have gained momentum in so-
cial media studies are teacher professional learning (Lantz-Andersson,
Lundin, & Selwyn, 2018) and healthcare and medical/nursing educa-
tion (Cummings & Mather, 2017; Fox & Bird, 2017; Hamm et al., 2013;
Lewis et al., 2018). When investigating the educational benefit of single
platforms, a number of literature reviews have analyzed educational ben-
efits of Facebook and Twitter (Aydin, 2012; Barrot, 2018; Chugh & Ruhi,
2018; Gao, Luo, & Zhang, 2012; Hew, 2011; Manca & Ranieri, 2013,
2016b; Niu, 2019; Tang & Hew, 2017; Yang, Wang, Woo, & Quek, 2011;
Voivonta & Avraamidou, 2018), while other social media platforms like
WhatsApp, Instagram, and Pinterest have gained less attention in these
types of studies (Manca, in press; Pimmer & Rambe, 2018).
Despite a number of literature reviews on global social media phenom-
ena and on specific social media platforms, a comprehensive understand-
ing of social media in education across disciplines is still lacking. This study
aims to provide a preliminary analysis of the academic discourse on social
media, spanning more than two decades of relevant articles to position ed-
ucational science in this growing and interdisciplinary research context.

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THEORETICAL BACKGROUND
Along with extensive reviews of the literature, other methodological ap-
proaches like meta-analytic reviews (Skoric, Zhu, Goh, & Pang, 2016),
meta-synthesis (Mnkandla & Minnaar, 2018), and meta-analysis (Huang,
2018; Liu, Kirschner, & Karpinski, 2017) have been gaining impetus in
assessing the educational benefits of social media studies and related sub-
areas. While social network analysis and bibliometric and scientometric
analysis are established fields of analysis in the communication and infor-
mation sciences, they are relatively new methods of investigation in educa-
tion and social media studies (Basak & Calisir, 2015; Gan & Wang, 2015;
Gupta, Kumar, & Gupta, 2015; Lopes, Faria, Fidalgo-Neto, & Mota, 2017;
Zyoud, Sweileh, Awang, & Al-Jabi, 2018).
To date, these analyses have been concerned with a specific focus on
Facebook or on social media generally and have tended to focus on
literature from a single database. Among the first studies, Basak and
Calisir (2015) conducted a scientometric analysis of publication trends
in Facebook-related articles retrieved from the Web of Science (WoS)
database. The study showed that engineering, business and econom-
ics, and education were the top three most popular research areas in
the 2005–2013 time span (Basak & Calisir, 2015). Other authors have
analyzed publications on the topic of “Facebook and Libraries,” as cov-
ered in the Scopus database during 2006–2014, and reported that so-
cial sciences contributed the largest share of publications, followed by
computer science, engineering, medicine, business, management and
accounting, arts and humanities, and decision sciences (Gupta et al.,
2015). A third study conducted in China mapped the intellectual struc-
ture of social media research published from 2006 to 2013 in the China
Academic Journals Full-text Database. It revealed that the most common
subject was (1) news and media, followed by (2) library, (3) information
and digital library, and (4) computer software and applications (Gan &
Wang, 2015). The study also identified 10 clusters of research on social
media in the examined literature (i.e., change of media and its influence
on news dissemination; socialization of social media; social media and
public events; social media and user behavior; social media and commu-
nication; social media marketing and information sharing; social media
and knowledge management; social media and government; empirical
study on virtual community; and social media and library), which indi-
cate the range of topics in social media research in China. The relatively
dispersive distribution of research topics suggests the imbalanced devel-
opment on social media research in the country: For instance, research
topics in Cluster 1 (change of media and its influence on dissemination)

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are found to be at the core of the social media field in China and tend
to be mature, whereas topics in Clusters 2 (socialization of social media),
3 (opinion leader and emergency) and 6 (social media marketing and
information sharing) are still developing and will become new research
trends in the future. Another study employed mapping and bibliometric
analysis to investigate the most used social media platforms in the field of
psychology between 2004 and 2015 and assessed the growth in publica-
tions, citation analysis, international collaboration, author productivity,
emerging topics, and mapped frequent terms in 959 publications (Zyoud
et al., 2018). Results of the study show that personality psychology, experi-
mental psychology, psychological risk factors, and developmental psychol-
ogy were the main topics investigated in the examined studies related to
social media.
The mentioned studies, published in information management and
health sciences publications, demonstrate an interest in providing a
comprehensive overview of social media across diverse disciplines by
means of quantitative methods (i.e., bibliometrics and scientometrics)
applied to large numbers of publications. However, to our knowledge,
there is a lack of similar analyses of the educational use of social media as
an extensive phenomenon in the diverse disciplinary sectors. The aim in
this chapter is to position educational science in this growing and inter-
disciplinary research context and to explore possible interconnections
between research disciplines.
RATIONALE OF THE STUDY
This study builds on the work laid out in the previous section and inves-
tigates the academic discourse on social media. More specifically, we em-
ploy a mixed-methods approach, combining co-citation and bibliometric
analysis to analyze more than two decades of publications in social media
research identified in the WoS. These methods have been increasingly
suggested to deal with large corpora of text (e.g., Deerwester, Dumais,
Furnas, Landauer, & Harshman, 1990). They provide a methodological
framework to unveil underlying structures (co-citation analysis) that can
be used as a point of departure for further investigations of common ter-
minology, content topics, and interrelations between research areas.
Consequently, in the context of this exploratory study, our research
questions are formulated as:
1. What does the general academic landscape look like on the overarching re-
search topic of social media?
2. To what extent can we identify interconnections between research disciplines?

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3. How does the domain of educational science, in particular, compare to the
other academic disciplines?
Next, we provide an overview of our data collection procedures and two
types of analyses to orient the reader.
METHODS
DATA COLLECTION
We collected data from the WoS Core Collection, which includes fully in-
dexed cited references, authors, and author affiliations with sources dat-
ing back to 1900 (Web of Science, 2018). Our search terms were based on
those used in the most recent literature reviews of social media in education
(Greenhow & Askari, 2017) and were deliberately broad to cast a wide net
in the search. Search terms included a mix of descriptive social media terms
(i.e., “social media” or “social network” or “blog”) and specific names of
major social media platforms (i.e., “Facebook” or “Twitter” or “Pinterest”).1
The search was limited by document type (journal articles) and language
(English) so that only journal articles in English were included.2 There were
no publication year limits on the search. The 84,755 results were download-
ed in batches of 500 articles at a time and included abstracts, cited refer-
ences, and article citation. The batch files were then imported into the sta-
tistical software package R. The files were then merged and analyzed using
the R libraries bibliometrix, tm, topicmodels, and wordcloud.
While the “casting a wide net” approach has potential drawbacks, we
believe that this technique considers the difficulty in providing a unique
definition of “social media” and thus results in a mixed approach of de-
fining and dealing with the matter (Hampton, 2016; Obar & Wildman,
2015). Moreover, we purposefully wanted to be very wide in our search
and definition to possibly discover commonalities between disciplines.
Furthermore, to identify prominent journals from the domain of educa-
tional science, we used the Incite Journal Citation Reports, Social Science
Citation Index edition, in WoS. Consequently, we used the list of 239 jour-
nals ranked in Education & Educational Research to filter the out the
applicable publications from the entire data set, which resulted in 1,474
publications (1.74% of the total data corpus).
CITATION NETWORK ANALYSIS
As mentioned, we combined co-citation and bibliometric analysis to an-
alyze more than two decades of publications in social media research.
Citations are formalized, explicit (content) connections between different

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scientific works (Garfield, 1979). Figure 1 provides a schematic example
of this. Author A writes an article and cites the work of Authors B and C.
This constitutes a co-citation. Moreover, the observation that A has cited B
and C suggests that A has established a certain commonality or discussion
with the work of B and C. Hence, the work of the latter two authors can
be said to have a certain basis for discussion and possibly also acceptance
in the research community. On the basis of this information, the recorded
co-citation networks can be used as a first step to possibly identify authors
who cover a certain content area and to map the resulting (content-re-
lated) discussion (see de Solla Price, 1963; Griffith, Small, Stonehill, &
Dey, 1974). In comparison to other bibliometric methods, in (co-)citation
networks, the main topics are represented by researchers (clusters) who
are connected with each other and point out possible (content-related)
proximity and connections (see Garfield, 2006). These clusters were de-
termined using the Louvain method (Blondel, Guillaume, Lambiotte,
& Lefebvre, 2008). For the purpose of this study, we did not look into
the directionality of the underlying citation network and used binary, un-
weighted network data. Consequently, we are not able to distinguish be-
tween topical antecedents and successor, either by topic or by author. Only
articles that show similarities in covering the predefined, wide domain of
“social media” were identified.
At face value, this type of analysis cannot provide any more detailed
information on the actual content that is being published and underlying
discourse about the content matter. However, based on these networks,
preliminary topic domains, as represented by citation links, can be extract-
ed from the collected publications. This can then be used as a basis for
further, more detailed bibliometric analyses, which will be described next.
BIBLIOMETRIC ANALYSES
Based on the results of the co-citation networks, we used bibliometric anal-
yses to further analyze the collected data. This type of analysis enabled us
to deal with the large amounts of text data. More specifically, we employed
latent semantic analysis (LSA; Deerwester et al., 1990). LSA is a technique
in natural language processing, particularly distributional semantics,
for analyzing relationships between words. LSA assumes that words that
are close in meaning will occur in similar pieces of text. The more of-
ten terms appear together or are used in the vicinity of other terms in
a document, the more likely it is that they contextually belong together,
providing preliminary evidence for a shared common understanding and
terminology. Additionally, and focusing on journals from the domain of
educational science, we used latent Dirichlet allocation (Blei & Lafferty,

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2009). This method is also referred to as topic modeling (Alsumait, Wang,
Domeniconi, & Barbará, 2010) and has increasingly been used to analyze
the underlying topical structure of these big data sets (Chaney & Blei,
2012). Again, the underlying notion is to analyze larger corpora of text
and identify words that often occur in the vicinity of each other. The more
often certain words occur in combination, the stronger the suggested re-
lation between them. This in turn can be used to unveil concepts, termi-
nological connections, and topics within the text. To conduct these types
of analyses, we had to determine the number of anticipated topics we ex-
pected to find in the text corpora. Because this can be a challenging task,
previous research has suggested to use the so-called Gibbs sampling algo-
rithm to identify this structure, which is the most commonly used method
(Blei, 2012). In the context of this method, the anticipated number of
topics is assigned ex ante, before the actual topic structure is apparent.
For the purpose of this study, we ran the applicable analyses for five, seven,
and 10 ex ante topics. Then, to determine the best fit for the underly-
ing data, we qualitatively analyzed the results. As a result, we determined
which option best describes the underlying content patterns.
Figure 1. Schematic representation of a co-citation network
To summarize, we used the co-citation analysis as a first step to map the
academic landscape of social media research from articles identified from
the WoS. From the links that were established between authors, who cited
each other’s work, we could stipulate whether different distinguishable clus-
ters cover social media in their work. Moreover, we could identify, at the
author level, whether any co-citations across academic disciplines occurred,
which would suggest a discourse on common terminology. As a second step,

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and to then draw more detailed conclusions about the connections we were
seeing, we specifically looked at the content that was being published. This
allowed us to zoom in on the specific words and phrases that were used in
the individual content clusters. The methods described earlier provided us
with a toolkit to assign content topics and terminology to the content clus-
ters that were previously determined on the author level.
RESULTS
DEMOGRAPHICS
Overall, the data set contained 84,755 articles from a total of 170,179 authors.
The average number of coauthors was 4.18. These articles cover the time span
in the WoS database from 1908 until 20182 and exhibited an annual percent-
age growth of 4.2%. For the purpose of this particular set of analyses, we de-
cided to focus on articles starting from 1992 onward for two reasons: First,
about 99% of all articles were published after 1992, which makes sense given
that social media approached mainstream adoption in early 2000; second, the
WoS search only started to include abstracts, which we needed for bibliomet-
ric analyses, as of 1992. The final data set is described in detail next.
Figure 2 shows how the number of publications has changed over the
years. Moreover, Figure 2 also contrasts the publications from the entire
data set (solid line, left axis; N = 80,267) with the publications from the
identified educational journals in the WoS Core Collection (dotted line,
right axis; N = 1,474). For example, in 2018, there were 10,646 publica-
tions in the entire data set, and 172 of these articles were published in the
identified educational journals.
Figure 2. Publications per year

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As can be seen in Figure 2, both lines (denoting all publications and
publications in education journals) follow a similar upward trend. Yet,
while the topic domain of educational science is growing, the volume of
publications in the identified educational journals (N = 1,474) remains
low compared with the volume of publications overall analyzed in our
WoS search.
CITATION NETWORK ANALYSIS
As previously mentioned, citations are formalized, explicit (content) con-
nections, and co-citation networks can contribute to mapping (content-
related) discussion among authors (see de Solla Price, 1963; Griffith et
al., 1974). Figure 3 shows an example of a subset of 350 nodes of the co-
citation network.3 In this context, the nodes represent authors, and the
edges visualize whether one author has cited another author in his or her
publication. We chose this particular visual representation, which is repre-
sentative of the entire data set, because a depiction of the entire network
would not have yielded visually clear sociograms given their large size.
Figure 3. Subnetwork (N = 350) co-citation network
As can be seen in Figure 3, there are numerous communities, as high-
lighted by the shades of the nodes, covering the topics of our WoS search.
Furthermore, these clusters appear to be interconnected, as highlighted

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by nodes from different clusters showing a connection. This suggests that
(individual) authors have collaborated on the topic of social media. The
interpretation of this can be twofold. On the one hand, this could sug-
gest that different authors from different strains of the same discipline
have jointly written an article on social media. On the other hand, this
could also reflect the collaboration of representatives from two (or more)
disciplines, who conducted multidisciplinary research on a specific topic
from the wide domain of social media. We have also been able to iden-
tify some nodes on the periphery in Figure 3. A possible explanation for
their outlier status in the overall network is, among others, that they might
have been published in English in an otherwise non-English journal (e.g.,
Zeitschrift für Erziehungswissenschaft: German Journal for Educational Science,
Zdravstveno Varstvo: Slovenia Journal for Health Care).
To identify content domains in which the communities published, we
then investigated the titles of the 11,732 journals included in the data
set. As described in the methods section, using topic modeling, we ran
the analysis for an anticipated number of five, seven, and 10 ex ante top-
ics based on the largest sets of clusters. This resulted in seven topics (see
Table 1), which we assigned to the topic domains of (1) medicine, (2) ap-
plied science, (3) healthcare, (4) management, (5) information sciences,
(6) psychology, and (7) social sciences (as a broad category). However,
this was merely a preliminary analysis of the content being published. At
this level of granularity, it was possible to identify the indicated topic do-
mains. A more detailed analysis—for example, by increasing the number
of ex ante topics—would allow us to provide a more differentiated picture
of, for example, “information science” and whether this example would
also include the subdomain of “computer science.”
BIBLIOMETRIC ANALYSIS
Figure 4 provides wordclouds for the entire data set and for the subset of
articles from the educational journals. The wordclouds are constructed
based on the most commonly used words from all applicable abstracts.
Here we can see traces of terminology being used within the identified
topical communities, as described earlier. For instance, in Figure 4a, next
to terminology like “social” and “network, words such as “patients” and
“brain” appear dominant in the abstracts, which corresponds to the ap-
pearance of related terms like “medicine” and “medical” listed in Topic 1
(in Table 1).

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Table 1. Results of Topic Modeling: Journal Titles
Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7
study social use paper learning teachers students
social network social new online teaching study
support analysis media digital knowledge study results
factors research facebook education collaborative language academic
health networks educational practices activities development groups
participants school research people wiki writing peer
medical data technology young design teacher course
internet schools
c
ommunication literacy community participants significant
intervention within networking critical environment professional higher
college education education media tools blogs university

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Next, we were interested in the content of the identified educational
journals. As expected, and as illustrated in Figure 4b, the focus is now
considerably more on “students,” “learning,” “school,” and “teachers”—all
terms that appear dominant in the wordcloud. However, words like “net-
work” were also again present. Although in the context of this study, we
cannot make a clear judgment of whether this constitutes a topical overlap
or genuine interdisciplinarity, it certainly provides preliminary evidence
for the use of a common terminology (e.g., method or approach) to deal
with the topics of our WoS search across the different content domains.
a) All abstracts b) Abstracts of all identified
educational journals
Figure 4. Wordclouds
To attain a more refined picture of the content being published in
educational science, we then conducted topic modeling within the edu-
cation journal articles subset of our data. As indicated earlier, we ran
the analyses for five, seven, and 10 ex ante topics. Based on a qualita-
tive interpretation of the results, we decided that seven topics provided
the best fit for the underlying data. More specifically, while five topics
appeared to summarize the underlying topical structure too much,
seemingly combining distinctive nuances with each other (e.g., online
learning among students and teachers), 10 topics did not add any new
dimensions. Instead, the additional topics merely subdivided already
identified topics into smaller subsets. As can be seen from Table 2, the
seven topics cover different aspects.
Topic 1 deals with social support mechanisms and interventions
around the general topic of health. Topic 2 takes a social network per-
spective and investigates applicable social network patterns related to

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schools. Topic 3 specifically deals with Facebook and how its use possibly
related to communication patterns in an educational context. Topic 4
looks at digital media, how it can be (critically) used in practice, and its
potential for literacy. Topic 5 is concerned with collaborative online envi-
ronments and apparently focuses on activities such as writing (in wikis).
Topic 6 appears to focus on teachers’ professional development. Finally,
Topic 7 deals with students, academic groups, and peers in higher educa-
tion courses and universities.
DISCUSSION
This study builds on the work laid out in the previous sections and inves-
tigates the academic discourse on social media by providing a prelimi-
nary analysis of social media across diverse disciplines and how the field
of educational research is interconnected with these disciplines. Specific
research questions for this project were:
1. What does the general academic landscape look like on the overarching re-
search topic of social media?
2. To what extent can we identify interconnections between research disciplines?
3. How does the domain of educational science, in particular, compare to the
other academic disciplines?
Results show that there is a constant growing trend in the number of
publications concerned with social media both as a transversal topic and
in the educational sector, which demonstrates that social media research
continues to attract scholarly interest (Bodily et al., 2019).
Investigation of topics through citation network analysis revealed that
a number of topical communities map a varied research agenda from a
(wide) range of different disciplines. We have shown that the vast major-
ity of research on the indicated topics, at the moment, is conducted in
the topical domains of (1) medicine, (2) applied science, (3) health care,
(4) management, (5) information sciences, (6) psychology, and (7) social
sciences. It seems that medicine and health sciences are the prominent
sectors in our investigation of social media, unlike previous studies con-
ducted to map social media studies or a specific social media platform in
a domain (Basak & Calisir, 2015; Gan & Wang, 2015; Gupta, Kumar, &
Gupta, 2015; Lopes et al., 2017; Zyoud et al., 2018). For instance, other
analyses of Facebook-related literature in WoS or Scopus databases have
identified engineering, computer science, and business as the most prom-
inent topical domains (Basak & Calisir, 2015; Gupta et al., 2015).
Despite these differences, synthesis across studies provides an image
of the growing interdisciplinary and transversal nature of educational

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Table 2. Results of Topic Modeling: Educational Journals
Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7
study social use paper learning teachers students
social network social new online teaching study
support analysis media digital knowledge study results
factors research facebook education collaborative language academic
health networks educational practices activities development groups
participants school research people wiki writing peer
medical data technology young design teacher course
internet schools communication literacy community participants significant
intervention within networking critical environment professional higher
college education education media tools blogs university

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science concerned with social media. This observation is further sup-
ported by a bibliometric analysis of the scientific literature conducted on
Facebook that was indexed at the WoS Core Collection, from Thomson
Reuters, and linked to the research areas of Education/Educational
Research (Lopes et al., 2017). Results indicated that the educational do-
main shows links with other academic disciplines such as health sciences,
computer science, and linguistics. Our findings also align with studies
published in the educational technology sector that demonstrated a spe-
cific interest in social media in health care and medical/nursing educa-
tion (Cummings & Mather, 2017; Fox & Bird, 2017; Hamm et al., 2013;
Lewis et al., 2018). Yet, future studies need to investigate the range of
social media subtopics in order to understand the nature of social me-
dia studies in these disciplines and how they connect to and help shape
disciplinary-related educational research.
Another result of this study concerns the impact of social media re-
search in educational science. Articles in educational journals repre-
sented a very limited percentage of the overall research output (1.74%).
Nonetheless, further research is needed to determine whether this
percentage is consistent with the total number of articles published in
the global database of journals concerning other topics. It is also im-
portant to situate social media in education research in the landscape
of educational technology research broadly (Bodily et al., 2019; Bond,
Zawacki-Richter, & Nichols, 2019; Zawacki-Richter & Latchem, 2018) to
assess to what extent it is attracting scholarly interest and for what rea-
sons (e.g., learning benefits for new generations of students who are
regular users of these platforms, challenges posed by commercial social
media for educational use). For instance, five out of the top 20 cited
papers across all journals on instructional design and technology schol-
arship between 2007 and 2017 were on social media, which indicates the
growing interest in this topic within the educational technology sector
(Bodily et al., 2019). In a review of four decades of educational tech-
nology research published in the British Journal of Educational Technology
(Bond et al., 2019), for the years 2010–2018, the leading research topics
were learning analytics, online collaboration in higher education, and
mobile learning and social media. The latter were investigated as tools
to enhance student learning and students’ engagement, as well as to
expand professional learning, but the psychological challenges of adopt-
ing Web 2.0 were also noted. A similar study conducted on four decades
of research in Computers & Education (Zawacki-Richter & Latchem, 2018)
showed that the educational potential of social media for learning start-
ed to emerge during 1997–2006 (identified as networked computers as
tools for collaborative learning) and continued to increase in 2007–2016

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Beyond Disciplinary Boundaries
17
(e.g., in research concerning online learning in a digital age) as a steady
and more mature scholarly interest. By more closely examining studies
outside the field of education, educational scientists can benefit from
the methodologies and findings provided by other fields. The limited
presence of educational science in the results of this study suggests that
other fields have been, at the very least, more prolific in their studies of
social media and, at most, have theoretical frameworks, methodologies,
and findings that could inform work in educational science through the
cross-pollination of ideas.
Furthermore, the co-citation analyses revealed topic clusters and com-
munities, as represented by co-citation links between authors. This con-
stituted the first step in our analysis and provided a first indication of
whether we could identify different distinguishable clusters that cover so-
cial media in their work. Furthermore, this analysis also showed that these
clusters were, to varying degrees, interconnected, which supports the
findings of previous work in this area (Gan & Wang, 2015). Bibliometric
analysis and topic modeling further supported this claim. More specifi-
cally, while we were able to zoom in on specific details, particularly for the
domain of educational science, we also identified a “common ground”
among disciplines, at least with respect to terminology. Interestingly, this
common terminology may indicate common methodological approaches,
namely social network analysis, or social media platform as the focus of
study, namely social network sites.
These findings can have practical implications for scholars and practitio-
ners alike. Scholars can benefit from these types of analyses to identify au-
thors and topic clusters that might have otherwise been invisible because
of the ever-increasing number of publications on social media. On the one
hand, this can provide valuable input to both theoretical and empirical
considerations in a research domain that is inherently interdisciplinary.
On the other hand, it can help to position different disciplines and meth-
odological approaches in a wider context, adding another dimension to
our understanding of how research on social media evolves and how it is
shaped by which disciplines. Similarly, practitioners can benefit from these
analyses because they provide them with an overview of the current “state-
of-the-art” on social media. What are scholars currently discussing? What
are the latest findings on how to use certain aspects of social media in class
(e.g., using Facebook in schools, collaborative writing using wikis)? Are
there commonalities among disciplines that might suggest an “agreed-on”
methodology or setting? Being able to provide possible answers for these
types of questions can be useful for practitioners without requiring them
to delve into the specific details of academic research.

Teachers College Record, 121, 140303 (2019)
18
CONCLUSION AND LIMITATIONS
While our study provides valuable insights on how social media research is
discussed across diverse disciplines and in the field of educational science,
five limitations should be considered when interpreting the results and
designing future studies on the topic.
First, building on previous research (e.g., boyd & Ellison, 2007; Ellison &
boyd, 2013; Hampton, 2016; Kaplan & Haenlein, 2010; Obar & Wildman,
2015; Tess, 2013), we have included a wide variety of definitions of “social
media” over a long time period. To acquire a preliminary overview of pos-
sible underlying connections between disciplines, this has shown to be a
valuable methodological approach. However, for the purpose of identify-
ing more detailed and nuanced relations between, for example, informa-
tion and educational science, future research should also consider differ-
ences in definitions and apply a denser time frame.
Second, the focus of our work was on article abstracts and not on full
papers. Similarly, we focused on journal articles. While this is a good point
of departure, it concentrates on a limited amount of text that highlights
the key issues of a publication. More specifically, different disciplines use
different primary publications for the discourse. Whereas computer and
information science mainly publish in conference proceedings, media
studies predominantly use book publications as an output channel for
their work. Hence, being interested in commonalities and trying to show
a multidisciplinary picture of the underlying research, those conducting
future research should strive to also allow for multidisciplinarity in refer-
ences. Similarly, we only analyzed 239 educational journals that were in-
cluded in the Social Science Citation Index edition. This means that edu-
cational journals in the Emerging Sources Citation Index (a citation index
produced since 2015 by Thomson Reuters, and now by Clarivate Analytics,
which includes publications of regional importance and in emerging sci-
entific fields) were not considered for analysis. Casting a wider net would
provide a more nuanced view of how scholars from the domain of educa-
tional science study and investigate the realm of social media.
Third, WoS was the only database used in our search. The information
required to run the analyses (abstracts, references, etc.) was only avail-
able through WoS, thus ignoring research that appears in other quality
databases. As more databases offer the same data access, this study could
be repeated with expanded access to journals that are not currently acces-
sible in this manner.
Fourth, in this study we took a holistic view of the subject matter. Future
studies should consider in more detail (1) possible thematic changes over
time; (2) prominent scholars from the co-citation network and how they

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Beyond Disciplinary Boundaries
19
might influence the focus and direction of research and the impact of
educational technology research in other areas of social media research;
(3) how educational benefits of using social media are investigated in the
diverse disciplinary areas and not only in educational science (e.g., medi-
cal education, computer science education, business and marketing edu-
cation); and (4) relevance of the social media topic with regard to the
most prominent topics identified in the educational technology sector.
Finally, although our quantitative approach is very well suited to an-
alyzing large corpora of text (e.g., Deerwester et al., 1990), it can be
criticized for only “scratching the surface” of the underlying academic
discourse. Using this mixed-methods approach as a filtering mechanism
to get a comparatively quick overview of how a certain topic is discussed
across different, interrelated disciplines is very valuable. However, to fur-
ther zoom in on terminological differences and nuances, it should be
combined with more thorough and qualitative literature reviews (e.g.,
Manca & Ranieri, 2013).
NOTES
1. The final topic search terms were: ((((((((((((((((((((“social media” OR “so-
cial network”) OR “social networking”) OR “blog”) OR “weblog”) OR “Facebook”)
OR “Twitter”) OR “Pinterest”) OR “Instagram”) OR “Snapchat”) OR “wiki”) OR
“Reddit”) OR “social network site”) OR “YouTube”) OR “Vine”) OR “WhatsApp”)
OR “Voxel”) OR “Tumblr”) OR “LinkedIn”) OR “microblogging”)).
2. Please note that our search was conducted on November 7, 2018. We there-
fore do not cover any publications that were published after that date.
3. Layout: Kamada-Kawai, Node Color: Louvain Cluster.

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20
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MARTIN REHM attained his PhD at Maastricht University, the Netherlands.
He is currently the transfer manager at the Institute for Educational
Consulting at the University of Education in Weingarten, Germany. His
research interests include informal learning in social media, social oppor-
tunity spaces, and applying mixed methods to assess the educational value
of social media. His recent work includes a contribution to the American
Journal of Education entitled “Drinking From the Firehose—The Structural
and Cognitive Dimensions of Sharing Information on Twitter?”
STEFANIA MANCA is a researcher at the Institute of Educational
Technology of the National Research Council of Italy. Her research in-
terests are social media and social network sites in formal and informal
learning, teacher education, professional development and digital schol-
arship, and student-voice-supported participatory practices at school. Her
recent work includes a contribution to the journal The Internet and Higher
Education, “Snapping, Pinning, Liking or Texting: Investigating Social
Media in Higher Education Beyond Facebook,” and a coauthored an ar-
ticle in Publications, “Is There a Social Life in Open Data? The Case of
Open Data Practices in Educational Technology Research.”
DIANA L. BRANDON is an alumna of the Educational Psychology and
Educational Technology program at Michigan State University. She cur-
rently is the Distance Learning Coordinator at Charleston Southern
University in Charleston, South Carolina. Her research interests include
technology integration in K–12 and higher education and professional
development for teachers and higher education personnel. Her recent
work includes a brief paper presented at SITE 2019, “Not Your Mother’s
Professional Development: A Flexible Approach to Faculty PD” and a co-
authored article in Written Communication, “Multidimensional Levels of
Language Writing Measures in Grades Four to Six.”
CHRISTINE GREENHOW is an associate professor in educational psy-
chology and educational technology, Michigan State University. She stud-
ies various forms of learning with social media, the design of social-me-
diated environments for learning, and changes in scholarship practices
with new media. (More information at http://www.cgreenhow.org and
@chrisgreenhow on Twitter.)