Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

A computational model of transmedia ecosystem for story-based contents


Published on

A computational model of transmedia ecosystem for story-based contents. Jai E. JungO-Joun LeeEmail authorEun-Soon YouMyoung-Hee Nam.
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 simply 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 expansions 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 transmedia ecosystem is designed to process a large number of the contents, and to support high understandability of the complex transmedia patterns.

Published in: Education
  • Be the first to comment

  • Be the first to like this

A computational model of transmedia ecosystem for story-based contents

  1. 1. 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 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
  2. 2. 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.
  3. 3. 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
  4. 4. 10374 Multimed Tools Appl (2017) 76:10371–10388 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)
  5. 5. Multimed Tools Appl (2017) 76:10371–10388 10375 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]
  6. 6. 10376 Multimed Tools Appl (2017) 76:10371–10388 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
  7. 7. Multimed Tools Appl (2017) 76:10371–10388 10377 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 S0 → S2 Asynchronous Sequel Tightly coupled S1, S2 → P2 Synchronous Interquel Tightly coupled
  8. 8. 10378 Multimed Tools Appl (2017) 76:10371–10388 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
  9. 9. Multimed Tools Appl (2017) 76:10371–10388 10379 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
  10. 10. 10380 Multimed Tools Appl (2017) 76:10371–10388 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
  11. 11. Multimed Tools Appl (2017) 76:10371–10388 10381 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
  12. 12. 10382 Multimed Tools Appl (2017) 76:10371–10388 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
  13. 13. Multimed Tools Appl (2017) 76:10371–10388 10383 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
  14. 14. 10384 Multimed Tools Appl (2017) 76:10371–10388 Table 11 The Narrative Expansion Methods used in Star Wars Series Expansion CM CN CD S0 → S1 Asynchronous Sequel Tightly coupled S0 → S2 Asynchronous Sequel Tightly coupled S1, S2 → P2 Synchronous Interquel Tightly coupled P1 → P3 Asynchronous Prequel Tightly coupled S6 → S7 Asynchronous Sequel Tightly coupled S7 → S8 Asynchronous Sequel 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
  15. 15. Multimed Tools Appl (2017) 76:10371–10388 10385 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
  16. 16. 10386 Multimed Tools Appl (2017) 76:10371–10388 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). References 1. Aarseth E (2006) The culture and business of cross-media productions. Pop Commun 4(3):203–211 2. Boutemedjet S, Ziou D (2008) A graphical model for context-aware visual content recommendation. IEEE Trans Multimedia 10(1):52–62 3. Brooker W (2009) All our variant futures: the many narratives of blade runner: the final cut. Pop Commun 7(2):79–91 4. Chunwijitra S, Berena AJ, Okada H, Ueno H (2013) Advanced content authoring and viewing tools using aggregated video and slide synchronization by key marking for web-based e-learning system in higher education. IEICE Trans Inf Syst E96-D(8):1754–1765 5. Du J, Xu H, Huang X (2014) Box office prediction based on microblog. Expert Syst Appl 41(4):1680– 1689 6. Fienberg SE (2010) The prehistory of the center for statistics and the social sciences, with a prequel and epilogue. Stat Methodol 7(3):175–186 7. Harvey CB (2015) Fantastic Transmedia, chap. of Hobbits and Hulks: Adaptation Versus Narrative Expansion, pp 63–92 Palgrave Macmillan UK 8. Jenkins H (2006) Convergence Culture: Where Old and New Media Collide New York University Press 9. Jung JJ, You E, Park S (2013) Emotion-based character clustering for managing story-based contents: a cinemetric analysis. Multimedia Tools Appl 65(1):29–45 10. Krizanovich K (2010) The reboot: Franchise rejuvenation in the film-product life cycle. Ph.D. thesis City University, London 11. Long GA (2007) Transmedia storytelling: Business, aesthetics and production at the jim henson company. Ph.D. thesis Massachusetts Institute of Technology 12. Manning S (2005) Managing project networks as dynamic organizational forms: Learning from the tv movie industry. Int J Proj Manag 23(5):410–414 13. McKee R (1997) Substance, Structure, Style, and the Principles of Screenwriting. HarperCollins, New York 14. Meixner B, Matusik K, Grill C, Kosch H (2014) Towards an easy to use authoring tool for interactive non-linear video. Multimedia Tools Appl 70(2):1251–1276 15. Menard D (2015) Entertainment assembled: The marvel cinematic universe, a case study in transmedia. Liverty University, Master’s thesis 16. Mesty´an M, Yasseri T, Kert´esz J (2013) Early prediction of movie box office success based on wikipedia activity big data. PLoS ONE 8(8):e71,226 17. Moon S, Bergey PK, Iacobucci D (2010) Dynamic effects among movie ratings, movie revenues, and viewer satisfaction. J Mark 74(1):108–121 18. Phillips A (2012) A creators guide to transmedia storytelling:how to captivate and engage audiences across multiple platforms McGraw Hill Professional 19. Pratten R (2011) Getting started in transmedia storytelling: A practical guide for beginners CreateSpace 20. Scolari CA (2009) Transmedia storytelling: Implicit consumers, narrative worlds, and branding in contemporary media production. Int J Commun 3:586–606
  17. 17. Multimed Tools Appl (2017) 76:10371–10388 10387 21. Sharda R, Delen D (2006) Predicting box-office success of motion pictures with neural networks. Expert Syst Appl 30(2):243?254 22. Shmueli E, Kagian A, Koren Y, Lempel R (2012) Care to comment?: recommendations for commenting on news stories. In: Proceedings of the 21st international conference on World Wide Web, pp 429-438. ACM, ACM New York, Lyon, France 23. Tryon C (2013) Reboot cinema. Convergence: The International Journal of Research into New Media Technologies, vol 19 24. Xia F, Asabere NY, Ahmed AM, Li J, Kong X (2013) Mobile multimedia recommendation in smart communities: A survey. IEEE Access 1:606–624 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.
  18. 18. 10388 Multimed Tools Appl (2017) 76:10371–10388 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.