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A longitudinal study of intellectual cohesion in digital
humanities using bibliometric analyses
Muh-Chyun Tang
1
·Yun Jen Cheng
1
·
Kuang Hua Chen
1
Received: 31 March 2017
©Akade
´miai Kiado
´, Budapest, Hungary 2017
Abstract As digital humanities continues to expand and become more inclusive, little is
known about the extent to which its knowledge is integrated. A bibliometric analysis of
published literature in digital humanities was conducted to examine the degree of its
intellectual cohesion over time (1989–2014). Co-authorship, article co-citation, and bib-
liographic coupling networks were generated so SNA based cohesion analysis can be
applied. Modularity maximization partition was also performed to both co-citation and
“author bibliographic coupling” networks to identify main research interests manifested in
the literature. The results show that, as publications in digital humanities continue to grow,
its diversity and coherence, two hallmarks of interdisciplinarity, have shown signs of
becoming more robust. The co-author network, however, remained rather fragmented, with
collaboration mainly limited by language and geographic boundaries. The domain specific
practices in digital humanities that might contribute to such fragmentation was discussed.
Keywords Digital humanities · Co-citation analysis · Co-author network · Network
cohesion · Interdisciplinarity · Bibliographic coupling · Intellectual cohesion · Knowledge
integration
Introduction
Digital humanities (DH), formally known as humanities computing, is a field of research
mainly concerned with the intersection between computing and various disciplines in
humanities. On “What is humanities computing?” McCarty stated that “it is
&Muh-Chyun Tang
mctang@ntu.edu.tw
Yun Jen Cheng
yjcheng0314@gmail.com
Kuang Hua Chen
khchen@ntu.edu.tw
1
Department of Library and Information Science, National Taiwan University, No. 1, Sec. 4,
Roosevelt Road, Taipei 10617, Taiwan, ROC
123
Scientometrics
DOI 10.1007/s11192-017-2496-6
methodological in nature and interdisciplinary in scope…focusing both on the pragmatic
issues of how computing assists scholarship and teaching in the disciplines and on the
theoretical problems of shift in perspective brought about by computing” (McCarty 2005).
As an emerging area of research that draws research interests and expertise from multiple
disciplines, DH presents a fertile ground for the study of “knowledge integration” process.
One crucial aspect of interdisciplinarity, beyond the presence of knowledge of diverse
origins, is the integration of them into a coherent enterprise (Rafols and Meyer 2010;
Porter and Rafols 2009; Porter et al. 2007). The cognitive integration of concepts, theories,
methods and/or results from diverse fields is considered as the hallmark of interdisciplinary
research (Wagner et al. 2011; Levallois et al. 2012). According to Rafols and Meyer
(2010), “knowledge integration” is “… a process that is characterized by high cognitive
heterogeneity (diversity) and increases in relational structure (coherence)” where coherence
is defined by the extent to which specific topics, concepts, tools are interconnected. In a
bibliometrics context, coherence can be represented by whether the basic bibliographic
elements (e.g. authors, articles, keywords, or publication sources) in a set of published
literature form a tightly or loosely connected structure. While in Rafols and Meyer (2010)
network coherence is used to represent how well knowledge is integrated across formally
defined disciplinary boundaries, the network construct of “cohesion” has also been applied
to characterize knowledge integration among different subspecialties within a discipline
(Moody 2004; Carolan 2008) a research area (Gondal 2011; Levallois et al. 2012; Liu and
Xia 2015) or a scholarly community (Rawlings et al. 2015).
While research initiatives in DH often share the common methodological outlook, it
remains an empirical question whether DH as a field has consolidated or remained frag-
mented over time. Little is known about what kind of bibliographic network topologies
might result from the collaboration between computer sciences and humanities, whose
scholarly practices stand in direct contrast to each other in many aspects. Taking a bib-
liometric approach, this study aims to fill the gap.
Literature review
As a continuously evolving field, DH has attracted a wide range of knowledge interests and
expertise, which is manifested by its disciplinary and institutional diversity (Svensson
2010). Yet, the degree of cross-fertilization among these research efforts essential for
interdisciplinarity is less evident. A distinction is often made between multi-disciplinarity
and interdisciplinarity as the later suggests, besides the presence of diverse bodies of
knowledge, the integration and synthesis of them into a coherent whole (Wagner et al.
2011). Porter et al. (2007) defines interdisciplinarity as a mode of research activity that
integrates theories, concepts, techniques or data from two or more bodies of specialization
or research practice.
As cognitive integration taking place in the research process is difficult to observe at a
large scale, researchers often opt to infer knowledge integration in a field from the outcome
of the research, namely, its published literature (Wagner et al. 2011). Knowledge inte-
gration in literature can be observed both at the micro and macro level. At the micro level,
the diversity of author background and cited references represented within individual
articles are often taken as evidences of knowledge integration (Bordons et al. 2004; Porter
and Rafols 2009; Rafols and Meyer 2010). At the macro level, researchers interested in the
degree of intellectual cohesion of a field as whole often opt to examine the degree of
Scientometrics
123
cohesion or interconnectedness of its published literature or collaborative network. (Moody
2004; Moody and White 2003; Acedo et al. 2006a; Vidgen et al. 2007; Carolan 2008;
Gondal 2011; Levallois et al. 2012; Liu and Xia 2015; Rawlings et al. 2015).
Based on the assumption that the knowledge integration process in research commu-
nities depends heavily on the topology of the underlying social network, Moody used the
structure of collaborative (i.e. co-authorship of journal articles) network to trace the macro
structure of subspecialties in Sociology over time (Moody 2004). The concept of “structure
cohesion” or “connectivity” in network analysis was used to measure the degree of social
cohesion in Sociology. Moody (2004) discussed three types of network structures: star
production, small world, and structurally cohesive and surmised on the corresponding
collaborative practices each might represent. Moody (2004) believe that a structurally
cohesive collaboration network model signals the presence of “permeable theoretical
boundaries and generic methods” that allows scholars specialized in particular empirical or
theoretical skills to collaborate freely. He added that, if enough scholars engage in this kind
of cross-fertilization, mixing across multiple areas, there will be few clear divisions pre-
sented in the collaborative network (Moody 2004). A “star production network”, on the
other hand, represents a scholarly community where the cohesion of the network hinges on
a small set of prominent scholars or seminal works at the core, with most others located at
the periphery. A small-world network is where the local clustering is high, yet thanks to a
few boundary-spanning shortcuts, the average path length remains low (Milgram 1967;
Watts 1999; Watts and Strogatz 1998). Similarly, Carolan (2008) used network structure of
articles published by a leading journal in Education to examine how well the heteroge-
neous set of ideas and practices were integrated within the discipline. Besides
aforementioned three models, a “plural world model” (Condliffe Lagemann 1989) was
proposed. Being the most fragmented of all models, a “plural word model” manifested
itself in isolated components that lack of linkages to each other, suggesting a variety of
specialized research communities that contribute little to the integration of knowledge in
the field. One novel aspect of Carolan (2008) was the use of server log data, instead of
traditional bibliographic data, to generate the network, where the strength of relationship
between two articles was determined by the degree of overlap of their readership. The
resulting article interlock network exhibits the features of both small-world and structural
cohesive models. Lately in their study of the co-authorship network in the interdisciplinary
field of “evolution of cooperation”, Liu and Xia (2015) traced its trajectory from a few
local structures to a global structure of “chained communities” that demonstrated the
features of a small world model.
While there have been previous efforts to using co-citation (Leydesdorff and Salah
2010), and co-words (Wang and Inaba 2009) analysis to map the domain of DH, they were
relatively small in scale and did not address specifically the question of its intellectual
cohesion. In the present study, three types of network resulted from co-authorship, co-
citation, and bibliographic coupling were generated from published literature in digital
humanities. Social network analytical methods were then applied to measure the inter-
connectedness of these networks. Of particular interests are the degree of cohesion or
integration manifested in these networks. It is hoped that the identification of network
topology (see Fig. 1) in these networks would provide insights into the scholarly practices
such as the collaborative patterns, the degree of interdisciplinarity, as well as the state of
cognitive consensus within DH. Modularity-based community detection method was also
applied to help identify major research interests in the field.
Scientometrics
123
Data collection procedures
As DH is not a formally recognized filed in the main citation databases, it poses a challenge
to identify all relevant literature that constitutes its knowledge base. In this study, the
publications of DH were identified by both keywords search with Scopus, one of the largest
citation databases, and journals with an explicit digital humanities orientation. For the
Scopus search, the query (“digital humanities” OR “digital humanity” OR “humanities
computing” OR “humanity computing”), was used to search the field of title, keywords and
abstract, which resulted in a set of 1967 articles and book chapters. As it is likely that
publications in DH do not necessarily have those keywords, a complementary set of
articles was created by retrieving all the articles published in the following journals from
their websites: Journal of Digital Humanity, Digital Humanities Quarterly, International
Journal of Humanities and Arts Computing, Digital Medievalist, Digital Studies, Literary
and Linguistics Computing, among which only Literary and Linguistics Computing was
indexed by Scopus at the time of data collection. The list of journals was selected because
they are published by the members of the Alliance of Digital Humanities Organizations
(ADHO). The union of the two sets constitutes the “target set” from which bibliographic
networks can be generated and analyzed. The “target set” contains 2115 articles, 2787
authors, and 3469 keywords.
The bibliographic information of the articles, including title, author(s), source (journal,
conference proceeding, and book), author keywords as well as citation counts in the target
set were then downloaded for further analysis. Three types of bibliographic networks were
generated: co-citation, bibliographic coupling and co-authorship networks. The co-author
network was built based on the co-occurrence of author names appearing in the author field
for all articles in the target set. Name disambiguation was facilitated by the author ID
assigned by Scopus, for articles not indexed by Scopus, authors names were examined by
the researchers to ensure consistency. To study how the co-author network evolves over
time, a 5-year overlapping time slice was used to divide our target set, resulting in a total of
Fig. 1 Network topology in a bibliographic network
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123
22 co-citation networks (i.e. from 1989–1993, 1990–1994… to 2010–2014. See Fig. 2).
The same 5 year sliding window was applied to the co-citation and bibliographic networks.
To generate the co-citation networks, pair-wise matching of all the articles’ received
citations has to be conducted. This is done by using Google Scholar’s citation tracing
function. Google Scholar was chosen because it is the most comprehensive citation
database. As many of the publications in our target set have not been indexed by the other
two major citation indexes, WoS and Scopus, Google scholar became the only feasible
option to trace the citation each article in our target set has received. The citations received
by every article in our target set were identified and downloaded so pair-wise matching
could be performed to identify shared citations. Bibliographic coupling was enabled by
matching the reference list of article retrieved from Scopus, therefore only articles pub-
lished by journals indexed by Scopus were included in this part of analysis.
Data analysis
The longitudinal approach allows us to trace the trajectories of knowledge diversity and
integration in digital humanities over time. The “balance” aspect of diversity proposed in
(Porter and Rafols 2009) is measured by Gini index, which calculated on the distribution of
author assigned keywords. A higher Gini index indicates a few dominant topics addressed
by many different articles, a lower Gini index, on the other hand, signals a more balanced
and therefore diverse interested in the field. Social network analytical metrics were applied
to measure the degree of cohesion of the bibliographic networks. There commonly used
cohesion metrics were applied to assess the intellectual cohesion of the international
community of DH: the percentage of nodes remains in the largest component, clustering
coefficient, and average path length. A component is the maximal sets of nodes in which
every node can reach every other by some path, no matter how long the paths are. The
relative size of the giant component, the largest subgraph within a network, is often used to
indicate how interconnected a network is. Cluster coefficient measures the degree to which
one’s neighbors are also connected. Average path lengths represents, on average, the steps
it takes for a node to reach another node in the network. A high clustering coefficient,
coupled with short network diameter or average path length, signal the presence of a small-
world phenomenon.
Fig. 2 Sliding publication window to generate multiple network
Scientometrics
123
Another application of bibliographic analysis is the identification and visualization of
the subtopics within a field (see, for example, Morris and Van der Veer Martens 2008;
McCain 1990; White and McCain 1998). Modularity compares the observed fraction of
linkes within the cluster to expected fraction if links were distributed randomly. Modularity
maximization algorithm was performed to identify important sub-areas (Blondel et al.
2008). Centrality analyses were also conducted to identify the prominent actors in the
networks. This part of analyses was conducted at journal, article, and author levels. Of
particularly interest was to identify items that contribute most to the cohesion of the
networks.
Results
The diversity of research topics in DH
Drawing on Stirling (2007), Rafols and Meyer (2010) pointed out when measuring
diversity, one needs to consider three aspects, namely, the number of discipline cited
(variety), the degree of their concentration (balance), and how dissimilar these categories
(disparity) are. Previous classification based approach to measuring disciplinary diversity
often involved utilizing subject categories (SC) assigned by Web of Science (WoS) at the
journal level. (Rafols and Meyer 2010; Porter and Rafols 2009). Without the benefit of a
formal classification system such as SC in WoS used in (Porter and Rafols 2009), it is not
practical to determine the dis/similarity of subjects treated in the literature so the dimen-
sion of disparity was left out of our diversity analysis. Instead, we used author assigned
keywords instead as the indication of subjects treated in the literature of DH. The variety
and the balance of the keywords were measured to assess its diversity.
The keywords retrieved from our target set were first manually examined to control for
spelling variations. Figures 3and 4shows, respectively, the growth of publications in DH
and the gradual rise of the number of distinct author assigned keywords over time, indi-
cating the growing variety of research topics in DH (Fig. 4).
Gini index was then applied to measure the balance of the subjects covered in our target
set. Gini index is commonly used to measure the skewedness of the distribution. High Gini
index of the keyword distribution signal the existence of few dominant keywords. Figure 5
shows a gradually rising Gini indexing over time, which might be interpreted as a gradual
consolidation of research efforts. Yet notice that even with the gentle rising slope, the
degree of concentration remains low at below 0.35, which suggests considerably balanced
research interests in DH.
To examine the evolving of research interests in DH, we further divided the most
frequent author assigned keywords into three 9 year periods: 1987–1995, 1996–2004, and
2005–2014 (see “Appendix 1”). An interesting observation is the presence of keywords
related to social media (e.g. “Twitter,” “social network”), digital culture (e.g. “Web 2.0,”
“videogame, mobile devices”, and “design”) in recent years, which seems to suggest the
expanding of DH from computer processing of literature to the study of all aspects of
humanists concerns of different aspects of digital culture.
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123
Fig. 3 The growth of articles in DH
Fig. 4 The rise of distinct author assigned keywords over time
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1987-1991
1988-1990
1989-1993
1990-1994
1991-1995
1992-1996
1993-1997
1994-1998
1995-1999
1996-2000
1997-2001
1998-2002
1999-2003
2000-2004
2001-2005
2002-2006
2003-2007
2004-2008
2005-2009
2006-2010
2007-2011
2008-2012
2009-2013
2010-2014
Fig. 5 Gini index of keyword distribution over time
Scientometrics
123
Co-author network in DH
Percentage of nodes constitutes the giant component is often used as a basic indication of
network cohesion. As shown in Fig. 6, the percentage of the nodes in the giant component
in the co-author network hovered only below 20% even after discounting the isolates,
which is extremely low compared to other disciplines or research areas (see Table 1).
Notice the contrast is especially striking with sciences, medicine, and IT. The low per-
centage of nodes in the giant component, coupled with extremely high clustering
coefficient and modularity (Fig. 7), indicated that most collaborations took place at the
local level, lacking global “shortcuts” found in the small world model to hold the network
together.
A closer examination of the co-authorship network shows that, beyond the two largest
components, there were relatively few international collaborations. The largest component
was composed of scholars mainly from the U.S. (28.53), Canada (27.12%), U.K. (26.84%),
and Germany (10.45%); the second largest component was composed of scholars from the
U.S. (39.71%), the Netherlands (30.88%), and Japan (5.88%); and the third component was
composed almost entirely of Italian scholars (96.97%) see (Fig. 8).
As shown in Fig. 9, beyond the three largest components, the rest of the components are
very small. Furthermore, the dominance of single-country co-authorship becomes even
more salient in smaller components and the network is mainly highly fractured along
national boundaries. The distribution of the main participating countries in DH research is
shown in Fig. 10.
Co-citation network
Figure 11 shows the long-term trend of nodes in the giant component of the co-citation
network over time. A jump of the percentage of nodes in the giant component can be
observed in the early 2000, which then gradually levelled off. Notice also that there is a
significant portion of nodes are isolated. The dip in recent years might be more likely due
to the artifact of citation window than a sign of disintegration. Another two cohesion
measures, average geodesic distance and clustering coefficient seem to calibrate such
interpretation. A rather short average distance between nodes in the giant component,
hovering around 4. The cluster coefficient stayed steadily high at 0.60, indicating dense
local collaboration, which, coupled with the short average distance, fit the parameters of a
typical small world model.
Fig. 6 Percentage of nodes in the main component in the co-authorship network
Scientometrics
123
Table 1 Components and clustering coefficient across different fields. Sources:
a
Acedo et al. (2006a),
b
Newman (2001),
c
Moody (2004) and
d
Liu and Xia (2015)
DH Management and
Organization
a
Medicine
b
Physics
b
High energy
physics
b
IT
b
Sociology
c
Evolution of
cooperation
d
# of nodes 2787 10,176 152,0251 52,909 56,627 11,994 197,976 3670
Average degree 3.8 2.43 18.1 9.7 173 3.59 – 3.409
Main component (size) 354 4625 139,5693 44,337 49,002 6396 68,285 1127
Main component (percentage) 12.7 45.4 92.6 85.4 88.7 57.2 34.5 30.71
Size of second largest component 68 23 49 18 69 42 – –
Clustering coefficient 0.927 0.681 0.066 0.43 0.726 0.496 0.194 0.632
Scientometrics
123
Centrality analysis was also performed to identify important works in DH. Of particular
interest are publications with highest betweenness centrality relative to their degree cen-
trality as they play a relatively more important role in holding the network together (see
“Appendix 2”).
One earlier study of a small set of articles in digital humanities identified two main co-
citation clusters of journals, one made up of specialist journals devoted to computing
application in humanities, and the other, a group of library and information science
journals addressing the issues in digitalizing archives and libraries (Leydesdorff and Salah
2010). We visualized the source co-citation network using MDS where the distance
between nodes signifying the similarity of their co-citation profiles (Fig. 12). The size of
the nodes represents betweenness centrality, while different colors group frequently co-
cited sources using fraction algorithm in UCINET (Borgatti et al. 2002).
Fig. 7 Trends of clustering coefficient and modularity in co-authorship network
Fig. 8 Three largest components in the co-authorship network
Scientometrics
123
Several sources that have an explicit focus in DH were in the red group; they also
tended to have a higher betweenness centrality, indicating a strong bridging role that
reflects the breadth and reach of their contents. Sources with a distinct emphasis in
computation and computational linguistics tended to be marked in gray. The blue group
represents the specialized interests in media, museum, archeology, biology and geography.
The black group is not as easy to interpret, still we can spot several conference pro-
ceedings, especially those related to digital libraries in this group. Notice also that the
stress value is quite high, signaling a high distortion of the two-dimensional visualization
to the original data.
Fig. 9 Part of the co-authorship network. Nodes colored by nationality of author affiliations. Cross- country
linkages are marked. (Color figure online)
Fig. 10 Distribution of author country affiliation
Scientometrics
123
Modularity maximization based community detection procedure was performed to
identify densely connected subgroups within the co-citation network (Blondel et al. 2008)
(see Fig. 13). To interpret the modularity classes, author assigned keywords were extracted
and their frequency tallied. The keywords with higher discriminative value (i.e. TF-IDF
weight), that is, keywords appear frequently in certain classes but infrequently anywhere
else, were then selected by the researchers. The success of the modularity based classifi-
cation was mixed as some classes are more interpretable then others. The distinction of the
classes was especially difficult among class 6, 7, and 8 because of the significant over-
lapping of keywords related to various digitalization efforts. However, keywords explicitly
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
1990-1994
1991-1995
1992-1996
1993-1997
1994-1998
1995-1999
1996-2000
1997-2001
1998-2002
1999-2003
2000-2004
2001-2005
2002-2006
2003-2007
2004-2008
2005-2009
2006-2010
2007-2011
2008-2012
2009-2013
2010-2014
% of nodes in the giant component (not include isolate)
% of nodes in the giant component (all nodes)
Fig. 11 Percent of nodes within the largest components in co-citation network over time
Fig. 12 MDS representation of source co-citation profiles. Stress value =0.417
Scientometrics
123
associated with computational techniques were present in class 6, while class 7 seems to be
more about collection development (see Table 2).
Bibliographic coupling network
The bibliographic coupling network was generated by pair-wise comparison of cited ref-
erences retrieved from Scopus. A threshold of shared 4 citations in the reference lists was
set to dichotomize the network as a largest drop of the percentage of the nodes in the giant
component was observed between 3 (25.30%) and 4 (15%) shared items in the reference
lists. The threshold of 4 was used because it generated the highest modularity value.
Figure 14 shows the percentage of nodes contained in the giant component relative to the
size of the network over time. A sudden rise of nodes in the giant component was observed
in the early 2009–2013 period, signaling increased consensus about knowledge base in DH.
Likewise, modularity maximization graph partition (Blondel et al. 2008) was applied to
the bibliographic coupling network to identify subspecialties in DH, only instead of
individual articles/book chapters, authors were used as the unit of analysis
(Fig. 12) McCain (1998). The author bibliographic coupling network comprised individual
authors as the nodes (Zhao and Strotmann 2008). This is done so to increase the inter-
pretability of the partitions as the original network is more fragmented. To interpret the
modularity classes, prominent authors in each class, as ranked by their h-index, citation
count and centrality, were identified. Their specialties were determined by the researchers
according to their publications and affiliations listed in Scopus. As shown in Table 3, the
larger the classes, the more heterogeneous the topics were present, with the exception of
“author attribution”, which is also readily recognizable in the co-citation network. Notice
that the resulting modularity classes are more well-defined than those produced by the co-
citation network, especially those involved specific computing or digital technologies
(Fig. 15).
1
2
3
4
5
6
7
8
9
10
Fig. 13 Visualization of modularity classes in co-citation network, modularity =0.62
Scientometrics
123
Discussion and conclusion
In this study we set out to examine the degree of topical diversity and intellectual cohesion
in the emerging field of digital humanities as manifested in its published literature. Digital
humanities presents an interesting case for a bibliometircs based domain analysis because,
firstly, as a research area strongly influenced by technological advance, its identity and
scope are continuously redefined and debated; secondly, as an interdisciplinary field it
combines various disciplines in humanities and computing, whose scholarly cultures and
communication practices are in many aspects drastically different from each other (Ar-
chambault et al. 2005; Nederhof 2006). Without a domain specific index also makes it
difficult to demarcate the domain of DH’s published literature. We approach this issue by
Table 2 Top 10 modularity classes in co-citation network
Class Percentage
(%)
Specialties Discriminative keywords
1 6.4 General interests Infrastructure, digitalization, GIS, literary studies,
interdisciplinarity, HCI, data science, information retrieval,
digital heritage, social network, visualization, handwriting
recognition, genealogy, historical GIS, maps, mobile devices,
phonetics, cultural criticism, spatio-temporal analysis
2 4.84 Metadata Annotation, linked data, XML, concurrent markup, RDF, TEI,
ontology, electronic editing
3 4.4 User studies User studies, research tools, information retrieval, semantic web,
virtual research environment, scholarship
4 3.62 Authorship
attribution
Authorship attribution, computational stylistic, stylometry,
artificial intelligence, text-mining, textual analysis, feature
selection,
5 3.13 Literary theories Literary criticism, literary theories, computer criticism, Bakhtin,
electronic texts, versioning, computer games, multi-media,
structuralism, reception theory
6 2.98 Digital
libraries/text
analysis
Digital libraries, digital archives, cervantes project,
computational linguistics, corpus linguistics, algorithmic
criticism, automatic text analysis, authorship attribution,
orthographic variation, support vector machines, machine
learning, N-gram, object-oriented modeling, spelling,
simulation, training
7 2 Digital libraries/
infrastructure
Metadata, archive, electronic editions, markup SGML, digital
library, collection management, cultural heritage, cultural
institutions, earmark, image restoration, manuscript
restoration, visualization, multispectral imaging, text collation
8 1.42 Digitalization Humanities computing, digitalization, collaboration, digital
preservation, research infrastructure, digital curation, digital
methodology, digital philology, digital ecosystem
9 1.22 Scholarly
communication
Scholarly communication, bibliometrics, citation analysis,
disciplinary differences, web 2.0, interdisciplinarity, computer-
mediated communication
10 1.22 Dialectology Sociolinguistics, dialect, dialectology, dialectometry, phonetic
(dis)similarity, language change in time, spectrogram,
cochleagram, barkfilter, Danish, Dutch, German, American
English
Scientometrics
123
combining the inclusion of all journals affiliated with ADHO, DH’s most established
scholarly society, and keywords search with Scopus, one of the most comprehensive
citation databases available. Five year overlapping time slice was applied so longitudinal
trends could be observed. It has been pointed out that previous studies in interdisciplinarity
has been limited in their static view of the state of knowledge at a certain point in time
(Wagner et al. 2011). A longitudinal approach such as ours allows us to trace DH’s
trajectory of topic diversity and cohesion over time. The results show that the degree of
knowledge diversity is high, as demonstrated by the increase of distinct author assigned
keywords and the balance of its distribution. The presence of increasing publication
0
0.05
0.1
0.15
0.2
0
500
1000
1500
2000
1993-1997
1994-1998
1995-1999
1996-2000
1997-2001
1998-2002
1999-2003
2000-2004
2001-2005
2002-2006
2003-2007
2004-2008
2005-2009
2006-2010
2007-2011
2008-2012
2009-2013
2010-2014
Network size Rao(Size of main component/Network size)
Fig. 14 Percentage of nodes in the giant component in the bibliographic coupling network
Table 3 Top 10 modularity classes in author bibliographic coupling network
Class Percentage Specialties Author specialties
1 8.99 General interests HCI, cultural geography, anthropology, ancient history, archive,
social media, literary theories, rhetoric and narratives, mobile
phone, video game
2 5.86 Digital
infrastructure
Data mining, ontology, semantic web, metadata, OCR, name
entity recognition, cultural heritage
3 5.35 Author attribution Stylometry, author attribution, dialects
4 5.10 Digital libraries e-learning, e-book, digital libraries, HCI, information seeking,
mobile learning, mobile users, internet studies, intelligent user
interface, data curation, linked data, informatics, 3D imaging
5 3.71 Social media Social media, social network, new media and learning, mobile
data, social studies of technology
6 3.31 Data mining Machine learning, topic modeling, information retrieval,
collaborative research platform, data sharing, bibliometircs,
word sense disambiguation, visualization, social network
analysis
7 2.86 3D visualization 3D modeling, 3D scanning, remote sensing, GIS
8 2.7 Topic modeling Topic modeling
9 1.4 Library and
Information
Science
Digital libraries, information seeking, virtual reference services,
metadata sharing, open access
10 1.37 Text recognition
and analysis
Handwritten text recognition, historical spelling variants,
character recognition
Scientometrics
123
sources from diverse disciplines also attests to its expansion. A recent broadening of
interests from using computing as a methodology to addressing various humanists
engagement with digital culture was also reflected in the keywords (Svensson 2010).
The case for knowledge integration is, however, mixed. Network cohesion analysis
showed that, while both co-citation, and to a less degree, bibliographic coupling networks
have gradually grown to be more cohesive, co-authorship network has remained extremely
fragmented. The co-citation network is the most cohesive and exhibits the features of the
“small world” model of high local clustering and short average geodesic distances. The
trend toward more integration started in the early 2000 as demonstrated by noticeable
increase of the percentage of nodes in the giant component. Being relatively more cohe-
sive, the co-citation network afforded more domain analyses. Centrality analysis was
performed to identify articles, monographs and other publication sources that contribute
most to the integration of the network.
As for bibliographic coupling network, a sudden increase of the percentage of nodes in
the giant component was spotted in the 2009–2013 period, and continued to rise in the
latest, 2010–2014 period, which suggests a growing consensus on the field’s knowledge
base.
Besides structural cohesion, modularity maximization partition was performed on the
relative more cohesive co-citation and author bibliographic coupling networks to identify
important areas of research interests. For co-citation analysis, author assigned keywords
were used to help interpret the modularity classes. Keywords with higher discriminative
value (i.e. TF-IDF weight) to each class were considered to be more representative. As for
bibliographic coupling network, an “author bibliographic coupling” (Zhao and Strotmann
2008) approach was adopted where authors, instead of citing articles, were used as the
basic unit of analysis. This is to help increase the cohesion and interpretability of the
classes as the original network is rather sparse. Compared to topics or subjects, authors
Fig. 15 Visualization of modularity classes in bibliographic coupling network, modularity =0.51
Scientometrics
123
have shown to give a more refined representation of a particular research interest in
previous works of author co-citation analysis (McCain 1990;A
˚stro
¨m2007). Indeed, in the
case of DH, the bibliographic coupling induced author network gives a more well-defined
mapping of specialties than the co-citation network.
The co-authorship network was shown to be highly fragmented, consisting of numerous
small components that resemble the “plural worlds” model, without an extensive giant
component often observed in neighboring fields, such as digital libraries, for example (Liu
et al. 2008). The clustering coefficient is very high, indicating that collaborators tends to
form closely-knitted clusters. Furthermore, it was shown that they are fractured mainly
along geographic and language boundaries, with very few international collaborations. We
suspect that this is mainly due to the national or regional character in humanities (Nederhof
2006). The other reason might be the fact that “humanities” is an aggregate term that
encompasses a wide variety of disciplines who might not share the same research concerns
or methodologies. Additionally, it has been well known that humanities scholars tend to
work independently and co-authorship is relatively rare, which might also contribute to the
fragmentation. Even though we do observe a gradual increase of the average number of
authors per paper in recent years to about 2.3, it is still much lower than other fields.
One novel aspect of this study is to juxtapose three types of network: co-authorship, co-
citation, and bibliographic coupling, all of which are widely used for analyzing a research
domain, but have rarely been combined and compared (Boyack and Klavans 2010; Yan and
Ding 2012), especially for the specific purpose of examining the degree of intellectual
cohesion of an academic field. The fact that three different networks exhibit distinct
topologies in the case of DH suggests that cautions need to be taken when only one type of
network is used to assess disciplinary cohesion. It also points to the need to take into con-
sideration the domain specific scholarly practices that undergird its production and
dissemination of knowledge. Among all the forms of knowledge flow or exchange of ideas,
co-authorship is the most formal and demands the highest commitment. It is also most
explicitly social and arguably more effective for the exchange of implicit knowledge.
Because of its higher threshold for connection, it might not come as a surprise that DH’s co-
author network turned out to be the most disintegrated. Yet one wonders whether such a great
discrepancy in cohesion between the citation based (i.e. co-citation and bibliographic cou-
pling) network and the co-authorship network is peculiar to DH or widelyobservable in other
emerging areas of research. For example, a densely interconnected “core” of authors was
found to play a crucial role in network cohesion in other emerging areas of research (Liu et al.
2008; Gondal 2011; Liu and Xia 2015) is ostensibly missing in DH. And it remains an
empirical question whether the degree of cohesion in citation based network and co-au-
thorship network are better aligned with each other in those fields. We suspect that several
factors relating to domain specific scholarly practices in DH might help explain its lack of far-
reaching collaboration. In Whitley’s theory of intellectual and social organization of aca-
demic fields, the dimensions of “mutual dependence” and “task uncertainty” were used to
characterize the culture and practices of an academic filed (Whitley 2000; Fry 2006). The
“task uncertainty” dimension refers to the degree to which research questions, theoretical
framework and methodological procedures are shared among scholars, whereas “mutual
dependence” refers to the extent to which scholars have to rely on colleagues’ contribution to
advance one’s work. Traditionally, humanities research has been characterized by high task
uncertainty and low mutual dependence. It has also been shown that “corpus linguistics”, an
important branch in digital humanities, is characterized by considerable variations in research
goals and work procedures as well as high “technical uncertainty” (Fry 2006). These domain
specific circumstances might help explain the sparsity of the co-author network.
Scientometrics
123
There are obviously limitations to our study, most noticeable of which is the lack of
analysis of knowledge integration at the micro level, namely, the drawing of expertise from
diverse disciplines to tackle a research question, which can be measured by looking into
the composition of authors’ disciplinary backgrounds (Levallois et al. 2012) and distri-
bution of subject categories appearing in the reference list (Bordons et al. 2004; Porter and
Rafols 2009; Rafols and Meyer 2010). Future studies of the knowledge integration at the
work or team level will greatly complement the present research to provide a better
understanding of the nature of knowledge integration in DH. Such an endeavor, however,
will have to first overcome the challenge of the lack of coverage of DH literature in the
major citation databases. Related to database coverage issue is how the bibliographic
universe of a field can be defined, especially one that is highly interdisciplinary and
continuously evolving like DH. Lacking a comprehensive bibliography, as one applied in
(Leefmann et al. 2016) for the bibliometric analysis of Neuroethics, makes it difficult to
claim comprehensiveness of the publication of a field. Even though we have tried to be as
inclusive as possible in our selection of relevant literature by combining keyword search
and key journal selection, it is inevitable that some of the highly representative sources or
works might still be missed out, especially those published in non-English languages.
Using Google scholar as the source of co-citation data also entails inherent bias in the
scope of citing documents (Falagas et al. 2008). While these data limitations demand
cautions when interpreting the results, there is little reason to see an even more compre-
hensive dataset would lead to drastically different conclusions. If anything, the inclusion of
non-English literature is more likely to increase rather than reduce the fragmentation of the
co-author network. One might also point out the perils of not using a fixed citation window
when generating the co-citation networks. Indeed, using a fixed citation widow is more
theoretically sound, especially for fields where articles continue to be cited years after its
publications. But in our case, we believe that it does not pose a threat to the conclusion we
draw from the longitudinal data. It is concluded that the field of DH, when represented by
its co-citation network, has become more cohesive over time because the percentage of
nodes in the giant component has grown steadily (except the latest few periods because it
takes time for the later publications to be cited) and the average path length has also
become shorter, despite the fact that earlier networks were allowed more time to accu-
mulate citations, therefore, everything being equal, should have formed more cohesive
networks than later periods. However, this is not the case. On the contrary, the co-citation
in later periods were shown to be more cohesive. Furthermore, even with the growing
number of publications, the clustering coefficients remained steady, which is another
indication of growing cohesion over time.
Appendix 1
See Table 4.
Scientometrics
123
Table 4 The most frequent author keywords in three stages
1987-1995 frq 1996–2004 frq 2005–2014 frq
1 Humanities
computing
2 Humanities computing 8 Digital humanities 202
2 Applications of
microcomputers
1 SGML 6 Humanities 49
3 Art history 1 Dialect 4 Digital libraries 39
4 Artificial intelligence 1 Dialectology 4 Big data 32
5 CAI in literature 1 Dialectometry 4 Ontologies 25
6 Chaos theory 1 Digital libraries 4 Technology 22
7 Cognition 1 Electronic texts 4 Text mining 20
8 Collaborative writing
and exams
1 Literary criticism 4 GIS 19
9 Computing 1 Literary theory 4 Metadata 19
10 Conceptual
convergence
1 Metadata 4 Annotation 18
11 Concordances 1 XML 4 Collaboration 18
12 Creativity 1 Archive 3 Digitization 18
13 Databases 1 Link 3 History 18
14 Discourse 1 Multi-media 3 Linked open data 18
15 Education 1 TEI 3 XML 17
16 Electronic text
editions
1 Authorship attribution 2 Archives 16
17 Extrovert 1 Bakhtin 2 Information retrieval 16
18 Fuzzy sets 1 Browsing and navigation in large
hypermedia
2 TEI 16
19 Gender 1 Commercial text 2 Digital history 14
20 Human computer
interaction
1 Computer criticism 2 Semantic web 14
21 Humanities 1 Computer games 2 Social media 14
22 ideation 1 Computer-aided learning 2 Visualization 14
23 Interdiscursivity 1 Computer-assisted language
learning
2 Cultural heritage 13
24 Intertextuality 1 Content tagging 2 Database 13
25 Introvert 1 Corpus-based rule development for
lexical acquisition
2 Social network 13
26 LAN 1 Digital images 2 Authorship attribution 12
27 Language systems 1 Digital preservation 2 Interdisciplinarity 12
28 Lexicography 1 Document analysis 2 New media 12
29 Literary criticism 1 Document type definition (DTD)
design
2 Open access 12
30 Literary theory 1 Education 2 Corpus linguistics 11
31 Microcomputers 1 Electronic editing 2 Libraries 11
32 Minorities 1 Expert systems 2 Narrative 11
33 Musicology 1 French theory 2 Digital archives 10
34 Networks 1 Higher education 2 Humanities computing 10
35 Pseudonyms 1 Hypertext 2 Internet 10
36 Questionnaire survey 1 Image restoration 2 Methodology 10
Scientometrics
123
Table 4 continued
1987-1995 frq 1996–2004 frq 2005–2014 frq
37 Reader response 1 Image-based humanities
computing
2 Scholarship 10
38 Riddles 1 Improvisation 2 Videogames 10
39 Semantics 1 Industrial text 2 Crowdsourcing 9
40 SGML 1 Interactive 2 Natural language
processing
9
41 Social history 1 It concepts 2 RDF 9
42 TEI 1 Linguistics 2 Twitter 9
43 Text 1 Literary studies 2 Artificial intelligence 8
44 Text encoding 1 Literary text 2 Computational
linguistics
8
45 Textual criticism 1 On-line delivery 2 Computer science 8
46 University
computing
1 Pedagogy 2 culture 8
47 Performance 2 Design 8
48 Posthuman 2 Digital 8
49 Protocols 2 Digital media 8
50 Reception theory 2 digital scholarship 8
51 Research 2 Digital technologies 8
52 Resource discovery 2 Education 8
53 Sator 2 eResearch 8
54 Semantic mark up 2 Evaluation 8
55 Signifying 2 Human–computer
interaction (HCI)
8
56 Statistics 2 Information
technology
8
57 Structuralism 2 Manuscripts 8
58 Stylistics 2 Modelling 8
59 Text encoding and rendering 2 Research 8
60 Text/image coupling 2 Research
infrastructures
8
61 Textual criticism versus control 2 Social sciences 8
62 Textual studies 2 Academic libraries 7
63 Transcription/editing tools 2 augmented reality 7
64 Undergraduate 2 Data mining 7
65 Versioning 2 Digital preservation 7
66 Winbrill 2 Epistemology 7
67 Womens writing 2 Historical GIS 7
68 4D-CAD 1 Information
visualization
7
69 ABET 2000 1 Linguistics 7
70 Absorption 1 Literature 7
71 Accreditation 1 scholarly
communication
7
72 Aesthetics 1 Arts 6
73 AFS algebra 1 Audience 6
Scientometrics
123
Appendix 2
See Table 5.
Table 4 continued
1987-1995 frq 1996–2004 frq 2005–2014 frq
74 AFS structure 1 Computational
stylistics
6
75 American English 1 Data Analysis 6
76 Analysis Technique 1 Data visualization 6
77 Animation 1 Ebooks 6
78 Architecture 1 eHumanities 6
79 Artificial intelligence 1 Ethics 6
80 Artists-as-researchers 1 Mobile devices 6
81 Autocorrelation 1 Multimedia 6
82 Barkfilter 1 Museums 6
83 Best educational practices 1 Newspapers 6
84 Bibliometrics 1 Storytelling 6
85 Carboniferous 1 User studies 6
86 Cervantes digital library (CDL) 1 Web 2.0 6
Table 5 Significant works in DH co-citation network
Betweenness
centrality
Degree
centrality
1 The history of humanities computing 1
2 What is digital humanities and what’s it doing in English departments 3
3 If you build it will they come The LAIRAH study: quantifying the use of
online resources in the arts and humanities through statistical analysis of
user log data
7
4 Killer applications in digital humanities 2
5 Humanities computing as digital humanities 6
6 Stylistic analysis and authorship studies 8
7 A data structure for representing multi-version texts online 18
8 Information seeking by humanities scholars 16
9 Text-encoding, theories of the text, and the ‘work-site’ 19
10 Exploring erotics in Emily Dickinson’s correspondence with text mining
and visual interfaces
4
11 The state of authorship attribution studies: some problems and solutions 11
12 Marking texts of many dimensions 12
13 Toward modeling the social edition: an approach to understanding the
electronic scholarly edition in the context of new and emerging social
media
53
14 Enabled backchannel: conference twitter use by digital humanists 61
15 All the way through: testing for authorship in different frequency strata 15
16 Electronic scholarly editing 10
Scientometrics
123
Table 5 continued
Betweenness
centrality
Degree
centrality
17 Human computing - modelling with meaning 65
18 Digital infrastructure and the Homer multitext project 42
19 The inhibition of geographical information in digital humanities
scholarship
26
20 Pliny: A model for digital support of scholarship 29
21 eHumanities desktop—an online system for corpus management and
analysis in support of computing in the humanities
122
22 Deploying general-purpose virtual research environments for humanities
research
77
23 An evaluation of text classification methods for literary study 32
24 Open source critical editions: a rationale 24
25 The state of the digital humanities: a report and a critique 13
26 Meaning and mining: the impact of implicit assumptions in data mining
for the humanities
9
27 Speculative computing: aesthetic provocations in humanities computing 34
28 Texts into databases: the evolving field of new-style prosopography 62
29 Thinking about interpretation: pliny and scholarship in the humanities 22
30 Digital visualization as a scholarly activity 14
31 Blobgects: digital museum catalogs and diverse user communities 127
32 Transcription maximized; expense minimized crowdsourcing and editing
the collected works of Jeremy Bentham
38
33 The structure of the arts and humanities citation index: a mapping on the
basis of aggregated citations among 1157 journals
37
34 Visual GISting: bringing together corpus linguistics and geographical
information systems
70
35 Disciplined: using educational studies to analyse ‘Humanities Computing’ 27
36 Beyond digital incunabula: modeling the next generation of digital
libraries
23
37 Understanding the information and communication technology needs of
the e-humanist
28
38 Methodological commons: arts and humanities e-science fundamentals 44
39 Rabbit/duck grammars: a validation method for overlapping structures 59
40 Computational contributions to the humanities 217
41 Computer-mediated texts and textuality: theory and Practice 5
42 Towards information retrieval on historical document collections: the role
of matching procedures and special lexica
74
43 Mapping the English Lake District: a literary GIS 347
44 Making web annotations persistent over time 86
45 American digital history 169
46 A controlled-corpus experiment in authorship identification by cross-
entropy
21
47 Quantifying evidence in forensic authorship analysis 228
48 A framework for contextual information in digital collections 235
49 The digital humanities and humanities computing: an introduction 82
50 Annotating digital libraries and electronic editions in a collaborative and
semantic perspective
170
Scientometrics
123
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