ChapterPDF Available
DOI: 10.4018/IJCMHS.2019070102
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Volume 3 • Issue 2 • July-December 2019
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
Vasiliki G. Vrana, International Hellenic University, Serres, Greece
Dimitrios A. Kydros, International Hellenic University, Serres, Greece
Evangelos C. Kehris, International Hellenic University, Serres, Greece
Anastasios-Ioannis T. Theocharidis, International Hellenic University, Serres, Greece
George I. Kavavasilis, International Hellenic University, Serres, Greece

Pictures speak louder than words. In this fast-moving world where people hardly have time to read
anything, photo-sharing sites become more and more popular. Instagram is being used by millions of
people and has created a “sharing ecosystem” that also encourages curation, expression, and produces
feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide
information, engage with audience and connect to other museums Instagram accounts. Taking into
consideration that people may not see museum accounts in the same way that the other museum
accounts do, the article first describes accounts’ performance of the top, most visited museums
worldwide and next investigates their interconnection. The analysis uses techniques from social
network analysis, including visualization algorithms and calculations of well-established metrics. The
research reveals the most important modes of the network by calculating the appropriate centrality
metrics and shows that the network formed by the museum Instagram accounts is a scale–free small
world network.

Communities, Followers, Following, Instagram, Likes, Posts, Scale-Free Networks, Small Worlds, Social
Network Analysis, Top Museums

Instagram, the social photo and video sharing mobile application, now owned by Facebook, Inc.,
was launched in October 2010 (Gillen, Freeman, and Tootell, 2017) and since then it has enjoyed
impressive growth. Instagram provides its users an instantaneous way to capture and share their
life moments with their followers through pictures, videos and stories which can be edited with
various filters, organized with tags and location information and accompanied by a textual caption
(Weilenmann, Hilliman, and Jungselius, 2013). Nowadays Instagram community counts more than
800 million monthly active users, 500 million of daily active users and 300 million of Instagram
Stories Daily Active Users (Aslam, 2018).
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As social media platforms are growing in popularity, organizations and corporations are moving
quickly to integrate them into their marketing strategies (Constantinides, Romero, and Gómez Boria,
2018). Instagram and the other social media platforms provide to museums, galleries and other
cultural institutions, new opportunities to widen the distribution of their cultural offer in ways that
were unthinkable and accessible only in person before (Ciasullo, Gaeta, Monetta, and Rarità, 2015).
Thus, social network platforms allow cultural institutes and museums to present their collections,
demonstrate their core values, communicate their activities and exhibitions directly, reach people,
increase public engagement (Spiliopoulou, Mahony, Routsis, and Kamposiori, 2014; Gonzalez
2017), connect with other museums, build relationships and establish networks (Lazaridou, Vrana,
and Paschaloudis, 2015).
Museums are increasingly investing in human resources, money and time to create and maintain
a high profile social media presence (Adamovic, 2013). However, up to now, little research effort has
been devoted to investigate how museums are using Instagram in exploiting its features and possibilities
with the existing studies mainly focusing on visitors (Budge, 2018; Budge and Burness, 2018; Suess,
2014; Suess 2018; Weilenmann, Hilliman, and Jungselius, 2013) and museum performance (Lazaridou,
Vrana, and Paschaloudis, 2015), while the structure of Instagram virtual museum communities formed
are under-studied. The paper at first investigates the use of Instagram by the most visited museums
worldwide by recording and analyzing performance characteristics like number of followers, following
and number of posts, and number of likes of the ten last posts. All indexes provide evidence of the
popularity and the activity of the accounts.
Instagram users form social networks since an Instagram account (user) can follow the activity
of other Instagram accounts (users). An Instagram social network is asymmetric (directed), in the
vein that if an Instagram user A follows user B, B need not follow A back (Hu, Manikonda, and
Kambhampati, 2014). Social network analysis can help to explore the nature of interconnected
accounts (Wasserman, and Faust, 1994). Next, the paper performs a topological analysis of the
network of the Instagram accounts of the most visited museums at two stages. At the macroscopic
analysis museums’ communication patterns are revealed. Park, and Jankowski (2008, p. 62) mentioned
that, this is important, since “the discovery of information networks among web sites or among site
producers through the analysis of link counts and patterns, and exploration into motivations or contexts
for linking, has been a key issue in this social science literature.” At the microscopic analysis of the
network the study identifies the central accounts that may have important implications as they act as
leaders where probably the most interesting conversation and exchange of information occurs. To our
knowledge, no research on the study of the social networks formed by museum Instagram accounts
has been reported, thus this paper attempts to fill this gap.
The rest of the paper is structured as follows. The next section presents a literature review on the
use of Instagram by museums while the third section provides a short introduction to social network
analysis. The fourth section presents the methodology applied and the fifth section discusses the
findings of the study and more specifically the performance of the accounts, the macroscopic view of
the museums’ network and the node level analysis of the network. Finally, conclusions and limitations
of the study are as well as future research directions are presented.

Social media have enhanced the capability of museums to increase public engagement, build
communities of interest around them, create “many-to-many” relationships, reach communities and
individuals, connect with visitors in a more meaningful way, perform marketing activities, get more
audience and communicate their exhibitions and activities (Angus, 2012; Fletcher, and Lee, 2012;
Kidd, 2011; Langa, 2014; Osterman, Thirunarayanan, Ferris, Pabon, and Paul, 2012; Spiliopoulou
et al., 2014; Tuğbay 2012). Few studies have focused on investigating how museums are using
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Instagram and exploiting its features and possibilities and on visitors and connection to exhibition
content through Instagram.
One of the first studies in the field is that of Weilenmann, Hilliman, and Jungselius (2013) who
investigated how Instagram is used to communicate visitors’ experiences while they are visiting a
natural history museum. Instagrammers work to construct their own narratives from their visits and
when they communicate their experiences using Instagram, they extend the reach of the museum
beyond its walls.
Suess (2014) explored the motivations of people using Instagram while visiting art galleries.
Through a case study of the Queensland Gallery of Modern Art, he found that when art exhibition
visitors use Instagram, they may share, promote and endorse the event to others and highlighted that
visitors “use Instagram in complex and meaningful ways” (p. 62). Later on, Suess (2018) investigated
the use of Instagram by visitors to an art gallery and the role it plays in their experience and found
that the use of Instagram at the gallery engaged visitors in a manner that transcended the physical
space and extended their aesthetic experience.
Studies have also focused on visitors and their connection to exhibition content through Instagram.
Budge (2017) investigated how museum visitors are using Instagram as part of their experience. His
investigation was based on a case study of an exhibition and employed visual content analysis to
frame, explore and interpret visual and textual posts by museum visitors using Instagram as part of
their experience. Findings suggest that museum’s visitors record details of their experience and draw
attention to exhibition content and specially to objects. Budge and Burness (2018) studied how visitors
engage with objects through Instagram. They recorded the visitors’ desire to communicate and share
their perspective and experience through photography. Arias (2018) using posts tagged to the Museum
of Islamic Art geolocation, identified graphic trends in the visiting narrative, situating the visiting
experience not only within the museum’s collection, but also within the social and cultural fabric of
the country and claims that visual media has the capacity to create preconceptions and expectations
about museum visiting experiences.
At a different approach Lazaridou, Vrana, and Paschaloudis (2015) investigated the use of
Instagram by the most visited museums worldwide; they described the activity and the performance of
museum accounts’ and recorded performance differentiations among the museums Instagram accounts.
To our knowledge, no research on the study of the social networks formed by museum Instagram
accounts has been reported. Thus, the originality of the paper lies in the study of the network formed
by museums interconnections using Social Network Analysis for first time while the limitations of the
study are associated to the sample, as the paper only investigates the list of the most visited museums.

Network theory or social network analysis (SNA) theory is a mature theory which can help exploring
the nature of interconnected unities (Wasserman, and Faust, 1994). The theory first emerged by
Moreno, a field anthropologist. Ever since the early 70’s, SNA is being studied within Graph
Theory, a branch of pure mathematics originated from Euler. Social Network Analysis has been
one of the fields with exploding research in the past twenty to thirty years, yielding considerable
literature, both in textbooks and journals. SNA ideas and results have been extensively used in
many applications and cases, ranging from structural anthropology to marketing and banking and
from viral infection to sociology.
Social networks can be defined as “a collectivity of individuals among whom exchanges take
place that are supported only by shared norms of trustworthy behavior” (Liebeskind, Oliver, Zucker,
and Brewer, 1996). In social media applications like Instagram, new online social networks emerge,
linking people, organizations, companies and knowledge and new ties are developed among people
sharing interests (Wellman, 2001).
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Social networks can be represented by graphs (also called networks). A graph/network depicts
useful relationships (Kydros, and Oumbalis, 2015). According to this approach, entities are
represented as nodes and any kind of interaction between two entities is shown by a line which
connects the two nodes that represent the interacting entities. A graph is thus comprised of a set
of nodes (or vertices or points) and a set of lines (or arcs, links, edges). The total volume of a
network depends on the absolute number of its vertices and edges. The density of a network is
the actual number of edges divided by the total number of edges that would completely connect
all pairs of vertices. A network is connected if a path exists between every pair of vertices;
otherwise it is disconnected in components. The shortest path between two vertices is the smaller
number of ‘hops’ needed to travel from one vertex to the other. The degree of a vertex is the total
number of its neighbors and it is the sum of in-degree (neighbors that point to) plus the out-
degree (neighbors pointed to) the vertex. The clustering coefficient of a vertex is a measurement
that computes the total number of edges within a node neighborhood divided by the maximum
possible edges in this neighborhood. Finally, a number of very useful metrics on nodes, such as
centrality measurements (degree, closeness, betweenness, PageRank, etc.) can show different
aspects of importance of a node among others.
Social network analysis can be used to identify patterns of interaction among the nodes and
knowledge flows within a social network, boundary spanners, gatekeepers, knowledge bottlenecks,
under- and over- utilized nodes (Ryan, 2007), along with central nodes that can act as hubs, leaders,
or bridging different communities (Albert, Jeong, and Barabási, 2000). Mead (2001) mentioned
that Social Network Analysis makes the invisible work visible. Moreover, the discovery of inherent
community structures can help understand networks deeply and reveal interesting properties shared
by the nodes (Zhao, Feng, Wang, Huan, Williams, and Fan, 2012).

The paper at first investigates the use of Instagram by the most visited museums worldwide. The ranked
list of the top most visited museums was searched. Data about sixty-nine top museums according
to the annually number of visitors were recorded between the 1st and the 3rd of July 2018. For each
museum of the ranked list its Instagram account was located and visited and the Number of Followers,
Number of Following, Number of Posts and Number of Likes of the Ten Last Posts were recorded
as shown in Table 1. All indexes provide evidence of the popularity and the activity of the accounts.
The following museums belong to the list of the top visited museums, but they do not have an
Instagram account and therefore they are not included in Table 1: Shanghai Science and Technology
Museum (Shanghai), National Palace Museum (Taipei), China Science and Technology Museum
(Beijing), Zhejiang Museum (Hangzhou), Chongqing Museum of Natural History (Chongqing),
National Folk Museum of Korea (Seoul), Shaanxi History Museum (Xi’an), Chongqing Science
and Technology Museum (Chongqing), China Art Museum (Shanghai), National Science and
Technology Museum (Kaohsiung), New Taipei City Gold Museum (New Taipei), National Museum
of Natural Science (Tokyo), Victoria Memorial (Kolkata), Auschwitz-Birkenau Memorial and
Museum (Oświęcim), Royal Łazienki Museum (Warsaw), Three Gorges Museum (Chongqing),
Fujian Museum (Fuzhou).
The network studied in this work has as nodes the museum Instagram accounts; if a museum
Instagram account follows another museum Instagram account then a directed line (arc) is drawn
from the node that represents the first Instagram account to the node that represents the second
Instagram account.
In order to construct this network, a java program was developed that found for each Instagram
museum account its Instagram followings that are included in the top museum list. The program
logic as pseudocode is the following:
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continued on following page
Table 1. Top museums and their Instagram accounts
Museum City Followers Following Posts Likes 10
Palace Museum Beijing 275 1 1 74
Louvre Paris 1.7m 260 1.486 392,258
National Museum of China Beijing 48 0 2 14
National Air and Space Museum Washington, D.C. 162.3k 67 1.113 12,060
Palace of Versailles Versailles 0 0 6 0
Metropolitan Museum of Art New York City 199 3 6 64
Vatican Museums Vatican City 7.203 34 11 3,960
National Museum of Natural History Washington, D.C. 676.4k 79 1.029 48,348
British Museum London 1.1m 424 1.512 174,637
Tate Modern London 2.2m 247 1.757 129,567
National Gallery of Art Washington, D.C. 273.4k 717 1.664 22,084
Peterhof State Museum-Reserve Saint Petersburg 25.5k 144 554 7,301
National Gallery London 859.7k 118 1.378 76,433
American Museum of Natural History New York City 232.9k 112 1.812 21,040
Natural History Museum London 221.7k 395 1.602 31,539
State Hermitage Museum Saint Petersburg 218.1k 484 2.944 36,208
Reina Sofía Madrid 54.5k 398 604 7,622
National Museum of American History Washington, D.C. 184.4k 733 2.97 14,212
Victoria and Albert Museum London 780.7k 216 2.187 48,103
Tsarskoe Selo State Museum-Reserve Saint Petersburg 30k 256 1.399 8,019
National Museum of Korea Seoul 15.5k 102 1.285 2,110
Centre Pompidou Paris 677.8k 199 1.449 19,640
London Science Museum London 163.1k 211 1.112 7,685
Somerset House London 98k 317 1.919 2,292
Musée d’Orsay Paris 459.3k 137 306 82,340
National September 11 Museum New York City 41.6k 104 1.192 11,756
Museum of King John III’s Palace at
Wilanów Warsaw 1.785 162 324 774
Nanjing Museum Nanjing 100 63 56 145
Museo del Prado Madrid 278.6k 145 294 102,059
National Museum of Natural Science Taichung 99 0 18 30
Kazan Kremlin State Museum-Reserve Kazan 2.111 3.7 1.013 186
Museum of Modern Art New York City 3.7m 648 3.174 116,438
National Gallery of Victoria Melbourne 211.1k 1.368 4.222 20,311
National Art Center Tokyo 16.6k 3 133 4,990
Royal Museums Greenwich London 10.4k 815 545 1,589
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For each museum m of the top museums list:
Record the Number of Followers;
Record the Number of Following;
Record the Number of Likes of the Last Ten Posts;
Construct the set of the museums that:
Belong in the top museums list AND follow museum m.
The network of museum Instagram accounts was represented by an adjacency matrix A. In our
case, A is a 69 × 69 non-symmetric binary data matrix, where the number 1 is placed in cell Aij if
the Instagram account i follows the Instagram account j; otherwise the number 0 is placed in the cell.
By using the adjacency matrix, the article performs a topological analysis of the network. The open
source package Gephi (Bastian, Heymann, and Jacomy, 2009) was used for the construction of the
directed graphs while NodeXL was used for calculating the community structures. At the macroscopic
analysis museums’ communication patterns are revealed. At the microscopic analysis of the network
the study identifies the central accounts that may have important implications as they act as leaders
where probably the most interesting conversation and exchange of information occurs.

Eighteen out of the sixty-nine most visited museums worldwide (26%) do not have an Instagram
account. Skewness is recorded both at the numbers of followers and following implying that there is a
tendency for some museums to have high values of the indexes while most museums have low values.
Table 1. Continued
Museum City Followers Following Posts Likes 10
Russian Museum Saint Petersburg 98.3k 157 924 14,194
Mevlana Museum Konya 470 680 225 1,101
Cité des Sciences et de l’Industrie Paris 5.519 2.658 687 416
National Museum of African
American History and Culture Washington, D.C. 149.9k 81 691 15,621
Battle of Stalingrad State Museum Volgograd 1.44 107 598 1,212
National Museum of Anthropology Mexico City 29.1k 31 564 3,722
Houston Museum of Natural Science Houston 28.9k 498 2.226 2,257
Galleria degli Uffizi Florence 147.9k 1.862 836 25,933
Rijksmuseum Amsterdam 215.3k 485 911 34,388
National Museum of Scotland Edinburgh 24.2k 1.503 992 1,428
National Museum of History Mexico City 43.3k 70 573 4,065
Shanghai Museum Shanghai 152 1 1 35
Van Gogh Museum Amsterdam 749k 269 1.27 157,389
California Science Center Los Angeles 11.8k 52 481 1,188
Tretyakov Gallery Moscow 182.9k 497 1.495 14,582
Topkapý Palace Istanbul 2.154 21 84 1,808
Likes 10: The likes of the last 10 posts
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The number of followers of an account shows how many other Instagram accounts have subscribed
to see the photos/videos/stories posted by the account and it is an indicator of success of the account.
This metric implies that the more followers an account has, the more impact the account has, as the
account seems to be more popular. De Veirman, Cauberghe, and Hudders (2017) studying Instagram
influencers referred to them as “people who have built a sizeable social network of people following
them” (p.798) while Jin and Phua (2014) mentioned that accounts that have a larger network of
followers are perceived to be more credible and popular. The number of following of a museum account
m, on the other hand, shows how many other museum accounts the account m has been subscribed to
follow. The number of others that an account follows is as equally important as the number of followers
when estimating the importance of an account in the sphere of the medium (Saito & Masuda, 2013),
like the Instagram-sphere in this case. In previous studies it was found that museums are following
a relatively small number of Instagram accounts, a fact that indicates museums do not engage with
the museum visitors (Lazaridou, Vrana, and Paschaloudis, 2015). However, the number of followers
and following provide an indication of the network expansion of an Instagram account.
Table 2 shows the descriptive statistics for the Posts, Number of Followers and Number of
Following indexes.
Museum accounts with low activity in Posts (Posts<10) or Followers (Followers<100) were
excluded from the analysis. More specifically, the museums: Palace Museum, National Museum of
China, Palace of Versailles, Metropolitan Museum of Art, National Museum of Natural Science and
Shanghai Museum where excluded from the analysis and the resulting dataset consisted of 45 museums.
The Museum of Modern Art New York has the maximum number of followers. Its network
has been continually expanding since the number of followers increased from 1,200,000 in 2014
(Lazaridou, Vrana, and Paschaloudis, 2015) to 3,700,000 in 2018. Tate Modern, the Louvre and
British Museum follow with 2.2, 1.7 and 1.1 million followers respectively. The mean number of
followers for a museum Instagram account is 357,621.822 and the Std. Deviation is 682,604.800.
As the standard deviation is much larger than the mean, there is a great dispersion of this particular
index among the museum accounts.
The Number of Following range from 0 to 2,658. The mean is 406.644 and the Std. Deviation
524.719. From Table 2 it is evident that museums accounts follow only few other Instagram accounts.
This finding is in accordance with the findings of Lazaridou, Vrana, and Paschaloudis (2015).
The Number of Posts is an indication of the activity of the account. National Gallery of Victoria
Melbourne is the most active account with 4,222 post followed by Museum of modern art New York
with 3,174 posts, while the mean number of posts is 1,191.177 (Figure 1).
The Number of Followers shows the maximum potential reach of an account, as the Instagram
algorithmic selection of posts may lead to a significantly smaller audience (Issac, 2016). Lazaridou,
Vrana and Paschaloudis (2015) also claimed that “not all the followers really “follow” the account
by means that they need not see and read labels of every post. (p. 80) thus a large number of
interactions may indicate engaging and interesting content (Gräve, and Greff, 2018). The number
of likes an account receives can be a strong measure of persuasion. Therefore, the Number of Likes
at Ten Last Posts was recorded as an indication of engagement. Louvre is the museum account that
Table 2. Descriptive statistics of Instagram accounts
Mean sd Skewness n
Posts 1,191.177 897.945 1,33 45
Number of Followers 357,621.822 682,604.800 3,61 45
Number of Following 406.644 524.719 2,7 45
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Volume 3 • Issue 2 • July-December 2019
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engages Instagram users and attracts a larger number of likes along with British Museum, Van Gogh
Museum, Tate Modern, Museum of Modern Art New York and Prado.
Figure 2 presents number of followers, posts and mean of likes at the ten last posts.
The following correlation matrix indicates significant bivariate coefficients mainly between
Followers and Mean of Likes and less between Followers and Posts.
The above relation (Table 3) is depicted in the scatter diagram of Figure 3.

The network formed among the museum Instagram accounts is investigated in this section.
Some museums do not follow and are not followed by any other museum. These museums
Figure 2. Number of followers, posts and mean of likes of the ten last posts
Figure 1. Number of followers (on the left) and numbers of following (on the right)

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correspond to isolated nodes and each one of them constitutes one network component. Isolate
notes create “a kind of noise in computations regarding the network as a whole, so it is a
common practice to be removed” (Kydros, 2017). Figure 4 presents the network in a circular
format. The nodes at the upper right of the figure have no connections to other nodes while
the vertices in the bottom area are more connected since their edges are denser. This is a first
hint for the existence of a non-random interconnection between nodes and thus the network
needs further investigation.
The network has 51 vertices that create 12 connected components. The larger component of the
network contains 40 vertices while the 11 isolated museum accounts correspond to single-vertex
connected components as shown in Table 4.
The Diameter, D, of the network is 4 and is the longest distance over all pairs of nodes. The
Average Geodesic Distance is 1.69, which is relatively small due to the fact that the network is small.
However, the coexistence of an Average Clustering Coefficient less than 1 (0.339) is an indication
that the network is a small world.
Table 3. Correlation matrix
Posts Mean of Likes Followers Following
Posts 1.00 0.19 0.42 0.24
Mean of likes 0.19 1.00 0.65 -0.08
Followers 0.42 0.65 1.00 -0.03
Following 0.24 -0.08 -0.03 1.00
Figure 3. 3D Scatter diagram between the number of followers, the posts and the mean of the likes of the ten last posts

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Reciprocated Vertex Pair Ratio is 0.215 and it is “the number of vertex pairs that have edges in
both directions divided by the number of vertex pairs that are connected by any edge” (Ranjan and
Sood, 2016, p.326) As the relationship “following” in Instagram is not mutual an “asymmetric” model
relationships exist (Zafiropoulos, Antoniadis, and Vrana, 2016) and only 21.5% of the relations in
the network are mutual.
Figure 4. The network in a circular format
Table 4. Overall network metrics
Graph Metric Value Graph Metric Value
Graph Type Directed Single-Vertex Connected Components 11
Vertices 51 Max Vertices in a Connected Component 40
Unique Edges 350 Max Edges in a Connected Component 350
Edges With Duplicates 0 Maximum Geodesic Distance (Diameter) 4
Total Edges 350 Average Geodesic Distance 1.69
Reciprocated Vertex Pair Ratio 0.215 Graph Density 0.137
Connected Components 12 Modularity 0.146
Average Clustering Coefficient 0.339

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
We now proceed in grouping the museums based on the country they are located. The resulting
network is shown in Figure 5. Not all countries are represented. There are a lot of interconnections
between countries.
In Figure 6 Country Groups are collapsed and vertex size is proportional to the number of museums
per group. Important information about the edges between the countries is shown in Table 4. Based
on the network shown in Figure 6 and taking into account Table 4 we notice that:
Some edges between countries do not exist. For example, China only points out but is not pointed
by anyone while France is pointed by many but only points to Japan;
U.S.A. museums are the most connected followed by U.K. museums. These museums tend to
follow each other but they are also following others;
Top museums in UK follow museums in France and USA and USA museums follow museums
in France.
These comments are based on the data in Table 5.

In Social Network Analysis, different groupings of nodes have been extensively used (Kydros, and
Anastasiadis, 2017) in order to investigate the “sub-structures” that may be present in a network.
These grouping usually follow quite strict rules and are rather hard to calculate (Wasserman, and
Faust, 1994). Girvan and Newman (2002) proposed the idea of communities of nodes. A group of
Figure 5. Museums grouped by country

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nodes belongs to the same community if more links exist between them than with other nodes outside
of this community.
The metric of modularity has been introduced as a measurement that corresponds to the quality
of grouping (Blondel, Guillaume, Lambiotte, and Lefebvre, 2008). Modularity measures the strength
of division of a network into modules. Networks with high modularity have dense connections
between the nodes within modules but sparse connections between nodes in different modules.
Modularity quantifies the quality of a given division of a network into communities thus it is often
used for detecting community structure in networks. The value of the modularity for the network of
museum Instagram accounts is 0.14 > 0, showing that the number of edges within groups exceeds
the number expected on the basis of chance (Li, and Schuurmans, 2011). The density, d, of a network
is the number of arcs in the network divided by the possible number of arcs (Faust, 2006). Figure 7
presents the five communities identified in the network:
All isolates are in one group (Group 3);
Group 2 is the most interconnected group, with a density of 0.394. This group is consisted of
History and Natural History Museums. Thus, a tendency is recorded that this type of museums
tries to connect each other;
Group 1 contains the Top Art Museums such as Louvre, Vatican Museums, Gelleria degli Uffici
and Russian museums;
Group 4 contains Museums in the U.K, Paris, Japan and Holland;
Group 5 contains museums from Mexico and Spain.

Watts and Strogatz (1998) and Barabási and Albert (1999) proposed ‘small-world networks’ and
‘scale-free networks’ respectively in an effort to define models of network structures differing from
regular and random networks (Erdös, and Rényi, 1959). Small world networks exhibit a small average
Figure 6. Country groups are collapsed

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path length between pairs of nodes and a high local clustering coefficient. Scale-free networks are
characterized by a highly heterogeneous degree distribution, which follows a “power-law” (Barabási,
and Albert, 1999). By zooming in on any part of the distribution does not change network’s shape,
in the vein that there is a small, but significant number of nodes with a lot of connections and there
is a trailing tail of nodes with a very few connections at each level of magnification.
In order to obtain the small world property, clustering coefficient should be between 0 and 1
and the average shortest path length should be smaller to that of a random network. For the museum
network the clustering coefficient is 0.339 and the average shortest path length (average geodesic) is
1.69, which in turn is smaller to that of a random network with the same volume. In a small world it
is very easy to navigate from one node to another, with a small number of hops.
Table 5. Edges within and between countries
From To Edges From To Edges From To Edges
USA
USA 40
China
USA 5
Italy
USA 1
UK 22 UK 3 UK 2
Russia 1 France 3 Russia 1
France 8 Holland 2 Spain 1
Holland 2 Spain 1 Australia 1
Australia 3 Italy 1 Vatican 1
UK
USA 28
Mexico
USA 2
Poland
USA 2
UK 30 UK 2 UK 2
Russia 3 France 1 Russia 1
France 11 Mexico 2 France 1
Holland 9 Holland 1 Holland 2
Spain 1
Holland
USA 11
Vatican
USA 1
Australia 2 UK 9 UK 2
Italy 2 Russia 1 France 2
Russia
USA 7 France 6 Holland 2
UK 8 Holland 2 Spain 1
Russia 13 Spain 2 Italy 1
France 8 Australia 2
France
USA 11
Holland 5 Italy 1 UK 7
Italy 1
Spain
USA 4 France 5
Poland 1 UK 4 Holland 4
Australia
USA 6 Russia 1 Spain 1
UK 4 France 3 Japan 1
Russia 2 Mexico 1 Australia 1
France 1 Holland 2 Italy 1
Holland 2 Spain 1 Vatican 1
Italy 1

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On the scale-free property, one needs to calculate the degree distributions and check whether
they follow the power-law. Pajek and R were used for this purpose and the outcome is presented in
Figure 8. Parameter x-min is set to 2 and alpha coefficient is computed to exactly 2, which lies on
the upper limit value. Thus the network is very close to a scale free network. In Figure 8 we present
Figure 7. Communities
Table 6. Communities after Clauset-Newmann algorithm
Label Vertices Unique Edges
Maximum
Geodesic
Distance
(Diameter)
Average
Geodesic
Distance
Graph Density
G1 12 45 3 1.361 0.341
G2 12 52 2 1.250 0.394
G3 11 -- -- -- --
G4 9 25 3 1.284 0.347
G5 7 13 3 1.265 0.310
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the degree distributions (left side) and fit them in the power-law on the right side. The almost straight
line on the right side implies that the network follows indeed the power-law property. Such networks
have the very interesting property of resilience to attacks, meaning that if a number of vertices are
removed, then the resulting network will still be connected. Such a network will be disconnected if
many high-degree nodes are removed for some reason.

In directed networks, like the one under investigation, it is important to rank vertices, in order to
identify the “important” individuals within the network as asymmetry in networks is assumed to be
linked to social prestige. Centrality measures capture a node’s position in the network. Well-established
centrality metrics are:
In-degree: The number of links going to a node (inbound links). It is a measure of high prestige
or a proof that others try to imitate the node and they represent support or influence;
Out-degree: The number links leaving a node (outbound links). Less important vertices or
vertices that have a very active but new social networking team do that;
Betweenness Centrality: Described as the number of shortest paths from all the vertices to all
the other vertices in the network that pass through the node in consideration (Brandes, 2001). It
is an indicator of a node’s centrality or importance in the network and shows the degree to which
the node mediates in information or controls the most information paths;
Pagerank: The former google-ranking algorithm for the popularity of web pages. It is a graph-
based ranking algorithm used to determine the importance of a vertex within a network by
considering both its inbound links and outbound links (Ding, Yan, Frazho, and Caverlee, 2009).
Figure 8. Scale free testing
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The metrics can show the most important variables in the network, according to the exact definition
of the metric (Newman, 2002, pp. 1-12).
Table 7 shows the museums with in-degree larger than 10. British museum, Museum of Modern
art and Louvre are the three most prominent museums since more than 20 other museums seek to
direct ties to them. These “elite” accounts act as “focal points” encouraging other influential museums
to congregate around them and thus attract audience to their account.
Figure 9 visualizes the graph taking into account the In-degree metric. Vertices with small in-
degree are smaller while vertices with high in-degree are larger.
Table 8 presents the museums with out-degree larger than 10. Natural History Museum, National
Museum of Scotland and Rijksmuseum are the top three museums with the higher out-degree. These
museums serve as nodes of useful information in the network as they may be relatively able to exchange
with others, or disperse information quickly to many others, and are often characterized as influential.
Figure 10 is the out-degree visualization. Again, larger vertices correspond to museums with
greater out-degree.
A number of museums accounts such as the Rijksmuseum, Louvre, National Museum of
American History, National museum of Natural History, National Gallery of Art, National Museum
of American History and National Gallery serve both as good hubs and good authority accounts. A
good hub account is one that points to many good authorities; a good authority account is one that
is pointed to by many good hub accounts.
Table 9 presents the museums with betweenness centrality above 10. Musee d’Orsay, British
museum, Museum of Modern Art, Museo del Prado and Peterfor State Museum – Reserve are the
nodes with higher betweenness centrality and have more control over the network, because more
information passes through that nodes.
Figure 11 is the visualization of the network according to the betweenness centrality measure.
Higher betweenness centrality is shown by larger vertices.
According to Pagerank, British museum, Museum of Modern Art, Rijksmuseum and
Louvre are the most important nodes in the museums network. Table 10 and Figure 12
present modes having Pagerank larger than 1.0; these nodes are the community leaders and
have huge influence on museums’ Instagram community (Wang, Zhang, Deng, Wang, Miao,
and Zhao, 2013).
In Figure 9 the graph is visualized taking into account the In-degree metric. Vertices with small
Pagerank are smaller while vertices with high Pagerank are larger.
Table 7. Museums ranked by their in-degree metric
Museum In-Degree Museum In-Degree
British museum 24 Van Gogh Museum 17
Museum of Modern Art 24 Rijksmuseum 16
Louvre 23 National Museum of American History 16
Tate Modern 21 Musee d’Orsay 13
National Museum of American History 20 Centre Pombidou 12
National museum of Natural History 20 London Science Museum 11
National Gallery of Art 18 American Museum of Natural History 11
National Gallery 18 State Hermitage Museum 11

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Figure 9. In-degree visualization
Table 8. Museums ranked by their out-degree metric
Museum Out-Degree Museum Out-Degree
Natural History Museum 21 Musee d’Orsay 15
National Museum of Scotland 19 Louvre 14
Rijksmuseum 19 Huston Museum of Natural Science 13
Royal Museum Greenwich 18 Russian Museum 13
Museo del Prado 17 Peterfor State Museum - Reserve 13
British museum 16 National Museum of American History 12
National Gallery of Victoria 15 National Gallery 12
National Gallery of Art 15 Museum of Modern Art 11
Van Gogh Museum 15 National museum of Natural History 11
Nanjing Museum 15

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
The paper at first aims to describe the performance of Instagram accounts of the sixty-nine most
visited museums worldwide. The methodology makes use of a prior original idea proposed to
measure Twitter accounts’ and blogs’ performance, so it transfers the relative knowledge to the
field of Instagram.
Figure 10. Out-degree visualization
Table 9. Betweenness centrality above 10
Museum Betweenness
Centrality Museum Betweenness
Centrality
Musee d’Orsay 116.922 Van Gogh Museum 42.760
British museum 113.470 Natural History Museum 41.323
Museum of Modern Art 112.819 National Gallery 39.622
Museo del Prado 100.561 National Gallery of Art 38.469
Peterfor State Museum - Reserve 82.407 Tate Modern 33.318
National Museum of American History 70.321 National Gallery of Victoria 29.295
National museum of Natural History 67.812 National Museum of American History 28.262
Louvre 61.240 Royal Museum Greenwich 19.118
Rijksmuseum 57.484 National Museum of Scotland 17.866

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From the sixty-nine most visited museums that were examined, fifty-one have an Instagram
account and were included in the research. Skewnness is recorded both at the numbers of followers
and number of following implying that there is a tendency for some museums to have high values
of the indexes while most museums have low values. This finding is in accordance with findings on
other social networks were skewnness has been recorded in some indexes (Antoniadis, Vrana, and
Zafiropoulos, 2014; Drezner, and Farrell, 2008; Theocharidis et al., 2015). Significant differentiations
Figure 11. Betweenness centrality
Table 10. Pagerank larger than 1.0
Museum Page Rank Museum Page Rank
British museum 1.889 Natural History Museum 1.434
Museum of Modern Art 1.839 Museo del Prado 1.433
Rijksmuseum 1.722 Tate Modern 1.376
Louvre 1.679 National Museum of Scotland 1.290
National Museum of American History 1.676 National Gallery of Victoria 1.256
Musee d’Orsay 1.666 Royal Museum Greenwich 1.187
National museum of Natural History 1.584 National Museum of American History 1.102
Van Gogh Museum 1.553 Peterfor State Museum - Reserve 1.028
National Gallery 1.486 Nanjing Museum 1.000
National Gallery of Art 1.482

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regarding activity have also been recorded, implying that some accounts are very active, while others
are only partially involved in spreading information and attracting users.
Next, the article investigated the properties of the network that is formed between the museum
accounts. The analysis uses techniques from Social Network Analysis, including visualization
algorithms and the calculation of well-established metrics. Investigation of connections in a social
network is crucial as they are the substrate over which information flows, which makes their flow
partially dictated by the network structure (Cardoso et al., 2017).
The network of the museums on Instagram is not mutual and an “asymmetric” model of
relationships exists. The network is very close to a scale free network. It turns out that the major
museum accounts are closely followed by smaller ones. These smaller museums accounts, in turn, are
followed by other museum accounts with an even smaller degree and so on. This hierarchy allows for
a fault tolerant behavior. As the network grows over time, museum accounts that already have a high
number of followers are more likely to see new followers (other museums in this case) established
to them, compared with other museum accounts with a lower number of followers. For a museum
account it is attractive to be connected to other museum accounts that are already highly connected.
The network of the museums on Instagram is a small world if isolates are removed. That means
that any museum account is on average connected to any other museum account in a small number
of steps. As museums network of acquaintances form a small world network, this has implications
for the spread of information, knowledge and ideas. Information, knowledge and ideas spread much
more rapidly in a small world network than they would through a network that does not have the
small world property. Small world properties enable museum networks to achieve great reach, high
bandwidth, and accelerate rate of knowledge creation.
Figure 12. Pagerank

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Communities are formed in the network showing a tendency of museums to connect each other
according to their type (History and Natural History Museums) or to their location. Thus, museums’
tend to interact with those museums with which they share similar traits and create ties with museums
that are similar to themselves in significant ways. Thus, information reaches museums of the same
type or the same location more quickly than the other museums.
Centrality metrics reveal the most important nodes in the network. Thus, the British museum,
Museum of Modern art and Louvre are the most prominent museums. If other museums Instagram
accounts congregate around them, and cooperate with authority museums will be closer to the
centers of community; may boost their Instagram visibility; attract more attention and audience
to their own account.
Natural History Museum, National Museum of Scotland and Rijksmuseum are the museums
that serve as nodes of useful information in the network, as they may be relatively able to exchange
with others, or disperse information quickly to many others. These museums serve as hubs in the
network, in the vein that they cite many related authorities, thus they are a useful resource for finding
authorities. They are influential and they play a key role in spreading information. This has obvious
implications on “word of mouth” and viral marketing, which in turn makes influential museums
important for the promotion and endorsement of new ideas.
Musee d’Orsay, British museum, and Museum of Modern Art, have more control over the network
as more information passes through them. Finally, according to Pagerank, the most important nodes
in the museums network are the British museum, Museum of Modern Art, Rijksmuseum and the
Louvre. Other museums should follow them in order to be aware of the information that flows in the
network and also to be motivated and inspired by them.
These distinct classes of measures assume different models of information flow on the network.
Museums may consider which measures are the most appropriate for them according to their strategy
and make their acquaintances and informed decisions based on them.
The limitations of the study are associated to the set of museums taken into account as the
paper only investigates the list of the most visited museums. Further research is needed taking in to
consideration all the museums that have an Instagram account.
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Vasiliki Vrana is an Associate Professor at the Department of Business Administration of Technological Education
Institute of Central Macedonia, Greece and serves at a tutor at the Hellenic Open University. She holds a BSc in
Mathematics, a MSc on information systems from the Hellenic Open University and PhD in Computer Science from
the Aristotle University of Thessaloniki. She was an Education Consultant at the secondary education for three
years. She has published more than 100 papers in international and Greek journals and proceedings that have
attracted more than 650 citations. Currently, she is doing research on Web 2.0 applications, especially blogging,
in tourism and politics and on education assessment.
Dimitrios Kydros is a Computers and Informatics Engineer from the Polytechnic School of Patras, Greece and
holds a PhD Degree in Applied Informatics from the University of Macedonia, Greece. He is currently an Associate
Professor at the Department of Accounting and Finance. His research interests include social network analysis
and applications, programming and algorithms and continuous education.
Evangelos Kehris holds a bachelor’s degree in mathematics from Aristotle University of Thessaloniki (Greece), an
MSc in Operational Research from LSE (UK) and a PhD in Computer Simulation from Lancaster University (UK).
Dr. Kehris has been employed as a research associate in research centers and universities in the UK and Greece.
Currently, he is professor at the Dept. of Business Administration at the Technological Education Institute of Central
Macedonia (Greece). His research interests include modelling, decision making and information technologies.
Anastasios-Ioannis Theocharidis holds a B.A. in Applied Informatics, University of Macedonia and an M.B.A. in
Hospitality and Tourism, Technological Education Institute of Central Macedonia. His work has been published in
refereed international scientific journals and conference proceedings. He is currently working as a social media
manager and his research interest include social media applications, especially in tourism, culture and music.
George Karavasilis received his BA in Mathematics from University of Ioannina, Greece. He holds a PhD in
Stochastic Point Processes from the Polytechnic School of Democritus University of Thrace and MEd from the
Hellenic Open University. His research interests include time series analysis, statistical software and educational
assessment. His work has been published in refereed international scientific journals and conference proceedings.
He is currently a high school director and an adjunct professor at the Business Administration Department of
Technological Educational Institute of Central Macedonia.
... The interactions in social media adapted from real life interactions such following, friending, sharing, mentioning, and many others shows how information flows in social media [19], [20]. The importance of information flow makes Social Network Analysis (SNA) paved its way to become a popular tool to analyze structure of social media network in the past years [17], [21]- [25]. In the other hand, tourism nowadays relies on social media promotion [26] and its important to understand information flow and connectivity about tourism or tourist in social media [25]- [28]. ...
... The importance of information flow makes Social Network Analysis (SNA) paved its way to become a popular tool to analyze structure of social media network in the past years [17], [21]- [25]. In the other hand, tourism nowadays relies on social media promotion [26] and its important to understand information flow and connectivity about tourism or tourist in social media [25]- [28]. This article aims to analyze tourism development in Maluku Province through the Instagram hashtag. ...
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... The interactions in social media adapted from real life interactions such following, friending, sharing, mentioning, and many others shows how information flows in social media [19], [20]. The importance of information flow makes Social Network Analysis (SNA) paved its way to become a popular tool to analyze structure of social media network in the past years [17], [21]- [25]. In the other hand, tourism nowadays relies on social media promotion [26] and its important to understand information flow and connectivity about tourism or tourist in social media [25]- [28]. ...
... The importance of information flow makes Social Network Analysis (SNA) paved its way to become a popular tool to analyze structure of social media network in the past years [17], [21]- [25]. In the other hand, tourism nowadays relies on social media promotion [26] and its important to understand information flow and connectivity about tourism or tourist in social media [25]- [28]. This article aims to analyze tourism development in Maluku Province through the Instagram hashtag. ...
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