A computational model of transmedia ecosystem for story-based contents

Multimed Tools Appl (2017) 76:10371–10388
DOI 10.1007/s11042-016-3626-5
A computational model of transmedia ecosystem
for story-based contents
Jai E. Jung1 · O-Joun Lee1 · Eun-Soon You2 ·
Myoung-Hee Nam3
Received: 3 May 2016 / Revised: 12 May 2016 / Accepted: 16 May 2016 /
Published online: 28 May 2016
© Springer Science+Business Media New York 2016
Abstract Story-based contents (e.g., novel, movies, and computer games) have been
dynamically transformed into various media. In this environment, the contents are not
complete in themselves, but closely connected with each other. Also, they are not sim-
ply transformed form a medium to other media, but expanding their stories. It is called as
a transmedia storytelling, and a group of contents following it is called as a transmedia
ecosystem. Since the contents are highly connected in terms of the story in the transmedia
ecosystem, the existing content analysis methods are hard to extract relationships between
the contents. Therefore, a proper content analysis method is needed with considering expan-
sions of the story. The aim of this work is to understand how (and why) such contents are
transformed by i) defining the main features of the transmedia storytelling and ii) building
the taxonomy among the transmedia patterns. More importantly, computational transme-
dia ecosystem is designed to process a large number of the contents, and to support high
understandability of the complex transmedia patterns.
Keywords Transmedia · Storytelling · Multimedia analysis · Digital contents ·
Computational ecosystem
These authors are equally contributed to this paper.
O-Joun Lee
concerto34@cau.ac.kr
1 Department of Computer Science and Engineering, Chung-Ang University, Seoul, 156-756, Korea
2 Department of French Civilization, Inha University, Incheon, 22212, Korea
3 Department of Theater and Film Studies, Inha University, Incheon, 22212, Korea

10372 Multimed Tools Appl (2017) 76:10371–10388
1 Introduction
Since information technologies have been dramatically improved and disseminated, most of
the production and consumption processes for digital contents have been diversified. It has
caused the advent of transmedia storytelling which is different from the existing storytelling
strategies limited in a single medium. Contents in transmedia ecosystems continuously
expand their stories from one medium to other media.
The term, Transmedia Storytelling is publicly used in academic and industrial area after
the remarkable successes of “The Blair Witch Project (1999)” and “The Matrix (1999)”
[8]. Jenkins [8] suggested Matrix series as a representative case of transmedia. From a
planing step, producers of this series tried to publish a huge story by distributing to mul-
tiple media. First, after they published their first movie, “The Matrix (1999)”, and then
they provided comics through the internet. Also, before they show sequels, “The Matrix
Reloaded (2003)” and “The Matrix Revolution (2003)”, they published an animation and
a computer game. They made hard to understand movies without watching/using related
contents. These series of story variations made users keep their attentions on the Matrix
series.
However, the transmedia can not be simply defined as expanding stories through multiple
media. Since not all the cases of transmedia are planned and started as a transmedia. In case
of Bourne series, when it was filmized at first time, it was a simple adaptation. It has became
a transmedia during expanding its stories, although it expanded in a single medium, movie.
Therefore, we propose a novel definition to describe the transmedia as not only expansions
of media, but also expansions of narrative worlds.
One of the reasons why the transmedia has a public attention is its continuous suc-
cesses. As shown in the Matrix series, the transmedia ecosystems become sustainable based
on the variations of stories. The variations of stories are not uniform, and as various as
theme and genre of the contents. Although the commercial successes of the transmedia
increases necessities to analyze it, varieties of story variations make hard to analyze. Also,
the huge amount of contents produced following the transmedia strategy makes a man-
ual analysis nearly impossible. For example, Marvel Cinematic Universe which is one
of the representative case is including dozens of movies and related with hundreds of
comics.
To address this problem, we propose a taxonomy to classify contents in the transme-
dia ecosystems following methods of the story variations. Also, we present a model which
can represent the transmedia ecosystems. This model represents narrative relationships
between the contents visually based on their temporal and saptial background, co-occured
characters, and co-occurred events. Finally, we suggest a method to automatically ana-
lyze story variations of each content. Contributions of this paper can be categorized as
follows:
1. suggesting the inclusive definition of the transmedia,
2. classifying the variations of stories used in the transmedia ecosystems,
3. and proposing a computational model to represent and analyze the transmedia ecosys-
tem.
The rest of this paper is composed as follows. In Section 2, looks into the previous def-
initions and taxonomies of transmedia presented by related works. Section 3 provides a
definition and a taxonomy proposed in this paper. In Section 4, we propose a computa-
tional model to represent and analyze the trasnmedia ecosystem automatically. Section 5
summarizes this study and suggests directions for future researches.

Multimed Tools Appl (2017) 76:10371–10388 10373
2 Understanding transmedia
A clear definition of transmedia is not settled in the academic area. It results from con-
tents based on transmedia and their strategies are not uniform. The existing definitions of
transmedia are focused on transitions and expansions of media. It can not include methods
and strategies to produce differentiated narratives according to media. Also, the taxonomies
of transmedia which are following this formulistic approach can not classify individual
narratives composing transmedia ecosystems in focus of their contexts.
2.1 Previous definitions
Jenkins [8] firstly has popularized and presented a concept of the transmedia. He brought
forth a question that most of the researchers had focused on the transmedia as an ‘Across
Media’whichsimplymeansastorytellingtechniqueapplyingmultiplemedia.Furthermore,he
suggested that transmedia apply multiple media to develop narratives and it is different from
cross media which is repetitively re-telling same narratives with simply changing media.
Also, he said that it is different from initial franchises like Mickey Mouse lunch box model.
From this point of view, he defined transmedia as: “a transmedia story unfolds across
multiple media platforms with each new text making a distinctive and valuable contribu-
tion to the whole [8].” It means transmedia storytelling is a new phenomenon which is
different from the traditional storytelling. Since contents composing them are providing
differentiated narratives and intimately related with each other in the same time.
Following Jenkins [8], Long [11] suggested that narratives in transmedia ecosystems
have independency and also tight-knit continuity with each other. Therefore, a general def-
inition of transmedia can be ‘a phenomenon that a narrative is transformed to separated
narratives based on various media and also they compose a bigger narrative worlds.’ The
focus of these definitions can be categorized into two major issues:
– expandability of media which means,
– and off-center nature which represents.
However, distinctively from the former definitions, Long [11] and Scolari [20] defined
transmedia in focus of a narrative structure. Long called a narrative published on a single
media as “self-contained.” He suggested the self-contained narratives have closed textures,
because they are not focusing on multiple media. Otherwise transmedia can expand their
narrative worlds, since they have open texture which enable a narrative to give birth to
another narrative. Also, Scolari defined transmedia as a particular narrative structure that
expands through different languages and media [20].
The previous definitions of transmedia have mostly emphasized appliances of media.
Most of the researchers have been careless for how variations of narratives are occurred
to expand narratives. However, we can not understand how narratives are differentiated
for each medium and construct close relationships with each other without considering
variations of stories. Thus, in this paper, we focus on how variations of stories contribute
on building sustainable transmedia ecosystems. Also, we propose novel definition and
taxonomy which focus on narrative expansions.
2.2 Previous taxonomies
Transmedia strategies are as various as the number of contents following them. They are
used differently according to relationships between contents, target media, major consumer

Table 1 Taxonomy according to
Media Expansion Methods (CM ) Criteria Category Examples
CM Asynchronous The truth about Marika (2007)
Synchronous Marvel Cinematic Universe, Matrix
series, Wars series
groups and so on. To find out patterns of them, few researchers suggested taxonomies of
transmedia strategies according to methods of expanding media, dependencies of narrative
fragments and so on.
Aarseth [1] categorized transmedia strategies according to methods of expanding media,
CM, as shown in Table 1. He focused on a simultaneity between the contents in transmedia
ecosystems, and categorized them as synchronous and asynchronous approaches. In terms
of the synchronous approaches, narratives are simultaneously published and closely related.
In this case, producers design narratives across various media, since they plan series of
contents in their transmedia ecosystem. On the other hand, in case of the asynchronous
approaches, producers publish their narratives sequentially with time intervals.
Phillips [18] classified transmedia strategies into big and tiny pieces according to a
dependency of the narratives, CD between media, as shown in Table 2. In case of big pieces,
contents in the transmedia ecosystems are whole and independent by themselves. However
in case of tiny pieces, the contents are dependent on a larger flow of narratives composed by
the ecosystems. Also, they have relatively tiny scale and are published simultaneously on
multiple media. A major difference of these two categories is a strength of linkage between
the contents. Since in the tiny pieces strategies, the narratives of each content is a compo-
nent of the narrative told by the entire ecosystem, the linkages between the contents are
much harder than the others. To avoid misunderstanding, we will call these two categories
as loosely coupled and tightly coupled respectively.
Pratten [19] categorized transmedia strategies into transmedia franchise, portmanteau
transmedia, and complex transmedia experience, as shown in Table 3. He considered both
of criteria (i.e., CM and CD) presented by Aarseth [1] and Phillips [18]. The transmedia
franchise is a strategy which publishes narratives sequentially on multiple media. In this
case, all the narratives of the contents are independent excluding cases of prequel [6] and
sequel [17]. Secondly, portamanteau transmedia is a strategy which publishes narratives
through various media at the same time. Each medium contributes as a component of whole
transmedia ecosystem. Therefore to figure out overall narratives, users need to combine
all the fragments of narratives as a puzzle. Finally, complex transmedia experience is a
combined strategy of transmedia franchise and portmanteau transmedia. This strategy is
mostly attempted to give users diverse experiences.
These taxonomies are efficient to show strategical methods of transmedia in focus of for-
malism. However they have some limitations that they can not reflect contextual aspects of
Table 2 Taxonomy according to
Dependency of Narratives (CD) Criteria Category Examples
CD Loosely coupled Marvel Cinematic Universe
Tightly coupled The truth about Marika (2007)

Table 3 Taxonomy according to CM and CD
Criteria Category Examples
CM , CD Transmedia franchise Matrix series, 24(a TV series)
Portmanteau transmedia The Beast (2001), Why So Serious (2007),
The Maesters Path (2011)
Complex transmedia experience The Tulse Luper Suitcases
narratives. Therefore in this paper, we focused on variations of narratives which are compos-
ing an unitary narrative world. Also, we classify them following how they are transformed
and expanded.
3 Narrative-based definition and taxonomy for transmedia
The previous definitions and taxonomies of transmedia have focused on meanings of the
word, transmedia. Thus they have considered only expansions of ecosystems across media,
not expansions of narrative worlds. However multilateral and polymorphic transformations
of stories are the most distinguishing feature of transmedia which are not shown in other
similar concepts (e.g., Cross-media, One Souece Multi Use and so on).
Therefore in focus of expansions of stories, we define transmedia as “a strategy uses
variations of stories to expand narrative world across multiple media.” Also, we concentrate
on the variations of stories which make the transmedia ecosystems sustainable.
Following this focus, we suggest a novel taxonomy for transmedia with a novel criteria,
expansions of narrative worlds. Since the contents in transmedia ecosystem is basically sto-
ries, narrative relations and linkages between them are as important as methods of expanding
media and independency. Therefore we classified the transmedia strategies into 8 categories
by adding one more criterion, methods of expanding narratives, CN .
Spin off is a representative case of narrative expansion methods in the synchronous and
big approaches. Also, we classified asynchronous approaches into 7 categories: reboot, pre-
quel, sequel, interquel, midquel, sidequel, and paraquel following their linkages between
contents. These 8 categories of narrative expansion methods can be described as follows.
– spin off: keeping backgrounds of an original work and making a new narrative
independently based on characters and materials in original work [12]
– reboot: making a completely new narrative with denying a continuity with former works
[23]
– prequel: dealing with temporally previous narratives of original work [6]
– sequel: handling temporally posterior narratives of original work [17]
– interquel: telling narratives happened temporally between two former works [10]
– midquel: showing narratives sharing same temporal backgrounds with original works
[10]
– sidequel: sharing temporal backgrounds with original works, but focusing on other
characters [3]
– paraquel: sharing temporal backgrounds with original works, but presenting a fully new
narrative [7]

Table 4 Comparison between the Existing and Proposed Taxonomies
Criteria Category Examples
CM Asynchronous The truth about Marika (2007)
Synchronous Marvel Cinematic Universe, Matrix series,
Star Wars series
CD Loosely coupled Marvel Cinematic Universe
Tightly coupled The truth about Marika (2007)
CM , CD Transmedia franchise Matrix series, 24(a TV series)
Portmanteau transmedia The Beast (2001), Why So Serious (2007),
The Maesters Path (2011)
Complex transmedia experience The Tulse Luper Suitcases
CN Spin off Fantastic Beasts and Where to Find Them (2016)
Reboot Batman v Superman: Dawn of Justice (2016)
Prequel X-Men Origins: Wolverine (2009)
Sequel The Bourne Supremacy (2004)
Interquel Mad Max: Fury Road (2015), Star Wars:
The Clone Wars (2008)
Midquel Cinderella III: A Twist in Time (2007)
Sidequel Pirates Of The Caribbean: On Stranger Tides (2011)
Paraquel The Bourne Legacy (2012)
Table 4 presents the previous three criteria and the proposed one and their examples.
Star Wars series is one of the typical series of contents which are following the trans-
media strategies. It shows how we can keep developing narratives by using prequel, sequel,
spin off, and interquel. Also, it makes us realize differences between these mostly used nar-
rative expansion methods. To explain the proposed taxonomy, we present a real example
based on Star Wars series. First, Table 5 is a list of contents in a part of an Star Wars series’
ecosystem.
Second, Fig. 1 is illustrating the expansions of narrative world in the ecosystem of the
Star Wars series.
Finally, the method of narrative expansion on each path are tabularized in Table 6.
Table 5 List of a part of contents in the Ecosystem of Star Wars
Package Content Title Media Year
P1 S0 Star Wars episode 4: A New Hope Movie 1977
S1 Star Wars episode 5: The Empire Strikes Back Movie 1980
S2 Star Wars episode 6: Return of the Jedi Movie 1983
P2 S3 Shadows of the Empire Novel 1996
S4 Shadows of the Empire Cartoon 1996
S5 Shadows of the Empire Computer game 1996

Fig. 1 Expansions of Narrative World in a part of Star Wars Series
4 Computational model for transmedia ecosystem
There were various challenges to apply computational methodologies for multimedia anal-
ysis. However, these researches mostly focused on physical features or meta-data, not
contextual information of contents. So, in this paper, we propose a novel computational
model to represent contextual information of the contents in the transmedia ecosystems.
Also we propose a method to classify the narrative expansion methods of the contents.
4.1 Computational model
To make a computational model for transmedia, modeling it based on paths of narrative
expansions is efficient approach, as shown in Fig. 1. However by only using the paths, we
can not detect the narrative expansion methods. Therefore we propose a novel approach by
annotating contextual information of contents in the transmedia ecosystems.
As shown in Section 2, the transmedia strategies can be distinguished based on tem-
poral and spatial backgrounds, commonly appeared characters and events. In case of spin
off, even if characters or events of an original work are appeared in an adapted work, they
should not be main characters or main events. Also in case of paraquel, even though an
original work and an adapted work are sharing same temporal background, they should not
share main characters and events. Moreover, sequel and sidequel commonly present tempo-
rally following narratives of original works. However, sidequel may present narratives about
minor characters or extras of original works, while sequel will talk about main characters.
Based on the former examples, we built a preliminary computational model which can
annotate temporal orders and spatial sharing between contents in a transmedia ecosystem.
This study defines the model of the transmedia ecosystem in the following manner.
Table 6 The Narrative
Expansion Methods used in Star
Wars Series
Expansion CM CN CD
S0 → S1 Asynchronous Sequel Tightly coupled
S1, S2 → P2 Synchronous Interquel Tightly coupled

Table 7 Categories of Characters’ Positions
Category Notation Description
Main characters Main Characters which lead major events and solve conflicts [15]
Minor characters Minor Characters which serve to complement the major characters
and help move the plot events forward [15]
Extras Extra Characters which just appear in one scene or shot. Extras are
not roles that share a story with others but give a hint to
solve some problem or cause trouble temporally [15]
Definition 1 (Transmedia Ecosystem) The transmedia ecosystem is a complex of indepen-
dent narratives. Also, it has an open texture which enables each content to start with a new
entry point of narratives.
It is represented as a grid-type diagram, and each cell of the grid means each content in
the ecosystem. The diagram is filled in according to the 3 rules:
– locating the contents as nodes in chronological order from left to right,
– laying out the contents which are sharing common spatial backgrounds on same rows,
– and annotating commonly appeared characters and events by edges between the nodes.
The elements composing the proposed model are defined as below.
Definition 2 (Content) The content is a constituent unit of the transmedia ecosystem. It has
an independent narrative from each other, however it shares main streams of narratives with
other contents in the ecosystem. A α-th content in the ecosystem is referred to Cα.
In the proposed model, it is represented as a node which is allocated at an individual cell
of the grid.
Definition 3 (Publication Date) A publication date means when each content is published
on each media. In lots of cases, orders of publication dates and temporal backgrounds of
contents are not corresponded. However also, it is an important element which exposes
method of expanding narratives. Given a content Cα, it can be represented as tα.
To present it, we tagged publication dates of contents on each node.
Fig. 2 The Proposed Computational Model for Transmedia Ecosystem

Fig. 3 Ecosystem Expansion of Bourne Series
Definition 4 (Background) A background refers to temporal and spatial background of
each content in the ecosystem. It is sometimes explicit, but sometimes implicit. Given
a content Cα, spatial and temporal backgrounds of it can be represented as Tα and Sα
respectively.
Because of its relativity, the proposed model represents it relatively. In case of the
temporal background, we represented their chronological orders as columns of the grid.
If a content is as posterior as allocated at the right side. On the other hand, the spatial
background is expressed as rows of the grid. Contents which are sharing a same spatial
background are allocated in a same row of the grid.
Definition 5 (Character) A character means a personage which appears in contents in the
transmedia ecosystem. Characters comprise the body of story progression [13]. It can be
appeared or mentioned in a singular content or multiple contents [15]. An i-th character in
the ecosystem is referred to chi.
In the proposed model, we only annotated characters which are co-occurred between
multiple contents as an dashed-edge between nodes.
Definition 6 (Position of Character) A position of character indicates a role of a character
in a particular content. Characters has different positions according to contents, and these
changes are one of representative expressions of narrative expansion methods. Given a char-
acter chi at a content Cα, it can be represented as pi,α. Also, a change of chi’s positions
between Cα and Cβ can be notated as {pi,α −→ pi,β}.
The positions and importance of characters and events are categorically annotated. Fol-
lowing conventional manners, we classified positions of characters into 4 categories: main
characters, minor characters, and extras (Table 7).
Definition 7 (Event) An event refers to incident that happens between characters in the
transmedia ecosystem. An event can be described or mentioned in a singular content or
Table 8 List of the contents in
the Ecosystem of Bourne Series Content Title Media Year
B0 The Bourne Identity Movie 2002
B1 The Bourne Supremacy Movie 2004
B2 The Bourne Ultimatum Movie 2007
B3 The Bourne Legacy Movie 2012
B4 Jason Bourne Movie 2016

Fig. 4 Classifying Narrative
Expansion Methods according to
Spatial-Temporal Background
multiple contents. It is an effective ways to demonstrate a cohesive transmedia storytelling
[13]. An a-th event in the ecosystem is referred to ea.
The proposed model only represents the events which are narrated in multiple contents
and directly related with main stories. The co-occurred events are annotated as an dotted-
edge between nodes. Finally, the proposed computational model is configured as Fig. 2.
Also, Fig. 3 is an example of the proposed model of a real transmedia ecosystem, Bourne
series. The contents composing the Bourne series are tabularized in Table 8.
By using the proposed model, we can easily infer the narrative expansion method based
on whether spatial and temporal backgrounds are same or not. The narrative expansion
methods are distinguished into 4 quadrants from Q1 to Q4 as belows.
– Q1: Reboot, Midquel and Sidequel
– Q2: Paraquel, Sidequel
– Q3: Prequel, Sequel and Interquel
– Q4: Spinoff, Prequel, Sequel and Interquel
It can be illustrated as Fig. 4 where (1) refers to an equality between backgrounds.
δ(A, B) =
1 ifA = B
0 ifA = B
(1)
Fig. 5 An Example of Reboot, Midquel and Sidequel

Fig. 6 An Example of Paraquel
and Sidequel
However, using only backgrounds of contents is not enough to detect the exact narrative
expansion methods. Therefore to detect them with more details, we have to deal with co-
occurred characters and events between the contents.
In case of Q1, we can distinguish the sidequel from the others based on the co-occurred
characters. Contents following the sidequel use different protagonists from their original
work. However in the reboot and the midquel, protagonists of original works commonly
appear as a same role. On the other hand, the reboot and the midquel can be discrimi-
nated by the co-occurred events. Rebooted works narrate same events with original works,
though contents following the midquel deal with different events from original works. Let
us suppose that there is an ecosystem modeled as Fig. 5.
Where expansions between C1, C2, C3 and C4 are included in Q1, chi is co-occurred in
C1, C2 and C3, chj is co-occurred in C1 and C4, and ea is co-occurred event in C1 and C2,
we can infer C2 is a midquel of C1, C3 is a reboot of C1 and C4 is a sidequel of C1.
Secondly in Q2, we can discriminate between the paraquel and the sidequel based on the
co-occurred characters. In the paraquel, characters appeared in original works are not used.
However in the sidequel, minor characters in original works are used as main characters or
a protagonist. Let us suppose that there is an ecosystem modeled as Fig. 6.
Fig. 7 An Example of Spin off, Prequel, Sequel and Interquel

Table 9 List of the contents in the Ecosystems of Star Wars
Package Content Title Media Year
P1 S0 Star Wars episode 4: A New Hope Movie 1977
S1 Star Wars episode 5: The Empire Strikes Back Movie 1980
S2 Star Wars episode 6: Return of the Jedi Movie 1983
P2 S3 Shadows of the Empire Novel 1996
S4 Shadows of the Empire Cartoon 1996
S5 Shadows of the Empire Computer game 1996
P3 S6 Star Wars episode 1: The Phantom menace Movie 1999
S7 Star Wars episode 2: Attack of the Clones Movie 2002
S8 Star Wars episode 3: Revenge of the Syth Movie 2005
− S9 Star Wars: The Clone Wars Animation 2008
− S10 Star Wars episode 7: The Force Awakens Movie 2015
− S11 Rogue One : A Star Wars Story Movie 2016
Where expansions between C5, C6 and C7 are included in Q2 and chj is co-occurred in
C5 and C6, we can reason C6 is a sidequel of C5 and C7 is a paraquel of C5.
Finally in Q3 and Q4, we can distinguish between the prequel, the sequel and the
interquel easily by using their temporal orders. However the spin off is not discriminated
based on temporal backgrounds, but co-occurred characters. In case of the spin off, minor
characters of original works are used as protagonists. Contrastively in the other cases, main
characters or protagonists of original works are appeared as protagonists of them. Let us
suppose that there is an ecosystem modeled as Fig. 7.
Where expansions between C8, C9, C10, C11 and C12 are included in Q3 or Q4, chk
is co-occurred in C8, C9 and C11, chl is co-occurred in C8, C10 and C11 and chm is co-
occurred in C8 and C12, we can infer C9 is a prequel of C8, C10 is a sequel of C8, C11 is a
interquel of C8 and C10 and C12 is a spin off of C8.
Fig. 8 Ecosystem Expansion of Star Wars Series

Table10Co-occurredCharactersinStarWarsSeries
Characters
NameNotationS0S1S2S3S4S5S6S7S8S9S10S11
NameNotation
LukeSkywalkerCh0MainMainMainMainMainminor
HanSoloCh1MainMainMainMainminor
PrincessLeiaCh2MainMainMainMainMainminor
DarthVaderCh3MainMainMainMainMainMainminor
GrandMoffTarkinCh4minor
Obi-WanKenobiCh5minorextraextraMainMainMainMain
C-3POCh6minorminorminorextraextraminorminorextra
R2-D2Ch7minorminorminorextraextraminorminorextra
YodaCh8extraextraextraextraMainextra
ChewbaccaCh9minorminorminorextra
LandoCalrissianCh10minorminor
AnakinSkywalkerCh11extraMainMainMainMain
DarthSidiousCh12extraminorminorminorminor
JabbatheHuttCh13minorMain
BobaFettCh14minorMainMain
DashRendarCh15minorMain
QueenPadmAmidalaCh16MainMainMainMain
ShmiSkywalkerCh17minorminorminor
SioBibbleCh18extraextraextra
JarJarBinksCh19extraextraextra
MaceWinduCh20extraextraminorextra
DarthTyranusCh21extraMainextra

Table 11 The Narrative Expansion Methods used in Star Wars Series
Expansion CM CN CD
S1, S2 → P2 Synchronous Interquel Tightly coupled
P1 → P3 Asynchronous Prequel Tightly coupled
S7, S8 → S9 Asynchronous Interquel Tightly coupled
P3 → S10 Asynchronous Sequel Tightly coupled
P1, P3 → S11 Asynchronous Spin Off Loosely coupled
4.2 Analyzing the real transmedia ecosystems
In this section, we present a practical analysis using the proposed computational model with
the real transmedia ecosystem. As an example, we use the Star Wars series which is the
representative case of transmedia. Table 9 is a list of contents in the Star Wars series.
The Star Wars series has begun with ‘Star Wars episode 4: A New Hope’ in 1980, and
expanded to 12 works including not only movies, but also novels, animations and cartoons.
Based on contents in the Table 9, we composed a computational model as Fig. 8.
We can see that most of the movies in Star Wars series are following sequel or prequel.
However in other cases, it is not certain whether they are following interquel or spin off.
Therefore we need to refer to another data, co-occurred characters. Since there are too many
co-occurred characters, we annotated them as a table as shown in Table 10.
The 22 characters are co-occurred between contents in Star Wars series. A part of the
characters keep appeared as same positions, however most of them keep changing. In case
of P0 and P1, contents in same package are sharing almost similar characters. Also, P2 and
S9 are using same characters with P0 and P1, respectively. Otherwise, S11 is not sharing
any characters with other contents. It shows us that P2 and S9 are interquel, and S11 is spin
off. Finally by using the proposed method, we classified the narrative expansion methods
of each content as shown in Table 11.
This example shows us that the proposed model is an effective method to expose nar-
rative expansions of transmedia ecosystems. Variations of stories are major component of
the narrative expansions. The proposed model enable to structurize and visualize the vari-
ations of stories according to transitions of narrative elements (e.g., temporal and spatial
backgrounds, positions of characters and events).
5 Conclusion and future works
With the rapid growth and spread of transmedia, this topic is not only meaningful for aca-
demic areas, but also industrial area. Furthermore the digitalization of authoring, publishing
and distributing systems of contents makes the analysis of transmedia not only humanists’
work. Following these requirements, we proposed the computational model for transmedia.
The proposed model can represent and classify variations of stories clearly. It is meaning-
ful to structurize and visualize variations of stories, since they expose narrative expansions

of transmedia ecosystems. In focus of humanities, this study shows obvious differences
between the existing storytelling and the transmedia storytelling. Also, the proposed model
can be a tool to understand how transmedia ecosystems make themselves sustainable.
On the other hand, the proposed model enables to provide story-based services for all-
round lifecycle of contents from producing to distributing. It can be used to implement
decision supporting systems (e.g., recommender system, curation system, authoring-support
system, box office prediction system and so on) for both kinds of users (i.e., audiences and
producers) with considering the new trend, transmedia.
5.1 Recommender system
Various methods have proposed to recommend digital contents (e.g., movies, musics, soap
operas and so on) [2, 24]. However most of them are not appropriate for story-based con-
tents. Shmueli et al. [22] suggested a method to recommend stories. Nevertheless it can not
reflect narrative features of the stories, since the authors simply used a latent factor model.
Jung et al. [9] proposed a method to extract narrative structures of movies based on social
networks between characters. Although it can not explain the expansions of narrative worlds
between multiple contents either.
The proposed model can make recommendations for series of contents more accurate.
Even if there is a user who likes Stat Wars series, the user may not like all the contents in
that series. It can be caused by the variations of stories which is including transitions of
characters, subjects, backgrounds, and so on. The proposed model complements this weak
point of the existing recommender systems. Because, if we can detect the variations of
stories, it enables to estimate what kinds of variations users prefer.
5.2 Box office prediction and authoring support tool
Until now, the most popular approach of the box office prediction has been analyzing users’
behaviors in the web (e.g., micro blogs [5], Wikipedia [16] and so on). Few researches
are using image processing methods [21]. However these approaches can not reflect stories
of contents. Since the proposed model automatically detects the narrative expansions of
contents, it enables to find out similar cases from historical data. If there are the similar
contents which used the same variation of story and dealt with similar subjects or genres,
we can predict an expected profit of a target content based on them.
Also, this model can be used for authoring support tools. The existing authoring support
tools mostly have focused on physical areas (e.g., finding and editing detects) [4, 14]. The
proposed model enables let authors or producers know what kinds of variations of stories
are preferred by users. Furthermore it can be more specific with considering demographic
data of users.
5.3 Limitations and future works
However the proposed model is preliminary and susceptible of improvement. First, the
proposed model is only focusing on detecting methods of expansions. Therefore it is
appropriate to compare relationships between contents. Nevertheless comparing the whole
transmedia ecosystems is also important to provide services to users. In future work, we will
apply a network similarity metrics to compare the transmedia ecosystems with each other.
Second, the proposed model can not cover stories of each content. It makes detect-
ing relationships between the stories of each content and the whole narrative world of

ecosystems hard. To address these issues, further study shall take the existing story analy-
sis methods for a singular content to understand the relationships between the story of each
content and the whole narrative world of ecosystem.
Furthermore, the proposed model is not considering users’ participation. Narrative
expansions, open textures and users’ participation are major characteristics of transmedia.
However it is only focusing on narrative expansions and open textures which are exposed
by variations of stories. In case of American soap operas, the main channel of users’ partic-
ipation is social network services like Twitter. Also, monitoring SNSs is one of the major
approaches to predicting box offices [5]. In next study, we will address this problem by
applying SNS analysis.
Acknowledgements This work was supported by the Ministry of Education of the Republic of Korea and
the National Research Foundation of Korea (NRF-2015S1A5B6037297).
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Jai E. Jung is an Associate Professor in Chung-Ang University, Korea, since September 2014. Before joining
CAU, he was an Assistant Professor in Yeungnam University, Korea since 2007. Also, He was a postdoctoral
researcher in INRIA Rhone-Alpes, France in 2006, and a visiting scientist in Fraunhofer Institute (FIRST)
in Berlin, Germany in 2004. He received the B.Eng. in Computer Science and Mechanical Engineering from
Inha University in 1999. He received M.S. and Ph.D. degrees in Computer and Information Engineering
from Inha University in 2002 and 2005, respectively. His research topics are knowledge engineering on
social networks by using many types of AI methodologies, e.g., data mining, machine learning, and logical
reasoning. Recently, he have been working on intelligent schemes to understand various social dynamics in
large scale social media (e.g., Twitter and Flickr).
O-Joun Lee is in combined MS/Ph.D. course in School of Computer Engineering at Chung-Ang University,
Korea. He received the B.Eng. in Software Science from Dankook University in 2015. His research topics
are recommendation system on digital content by using sequential pattern mining, incremental clustering,
and social network analysis.

Eun-Soon You is a lecturer in Inha University, Korea, since September 2015. Before joining INHA, she
was a research fellow in Dankook University, Korea since 2011. She has M.S and PhD in Natural Language
Processing from Besanon University in France in 2001 and 2007. She also has M.S in French Literature
from INHA University in 1997. Her Research interests include digital storytelling, Big Data, Social media,
machine translation, ontology, text mining.
Myoung-Hee Nam is a lecturer in Inha University, Korea, since 2014. Before joining Inha, she was a
researcher fellow in Dankook University, Korea, since July 2011 to May 2013. She has M.S. and Ph.D in
Film Department in Hanyang University, Korea in February 2000 and September 2007.She wrote a book
about US TV shows(ISBN 978-89-92214-94-0). She is interested in Film, TV show and Fandom study.

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