29 Storytelling on the Web 2.0 as a New Means of Creating Arts 633 Fig. 3 A comparison of the existing storytelling platforms to drive on it. The system can be interpreted as community of practice (drivers who have access to the Internet via mobile phones or PDA’s) and collaborative, since it is very important to get real time feedback from the users. Figure 3 presents a summary of all systems presented. Features presented in the table are very important for one storytelling system nowadays to meet all the re- quirements of the users. YouTell: A Web 2.0 Service for Community Based Storytelling How to apply storytelling for professional communities can be enabled by Web 2.0 and Social Software. We have designed and developed youTell using Web 2.0 ser- vice for community based storytelling. It is based on a social software architecture called Virtual Campfire. Virtual Campfire In order to make knowledge sharing a success for any kind of professional com- munity, independent of size or domain of interest, a generic community engine for Social Software is needed. After some years of experience, with the sup- port of professional communities two different products emerged: a new reflective research methodology called ATLAS (Architecture for Transcription, Localization, 634 R. Klamma et al. and Addressing Systems) [10] and a community engine called LAS (Lightweight Application Server) [23]. The research challenge in ATLAS was to incorporate the community members as stakeholders in the requirements and software engineering process as much as possible. In the end, all community design and engineering ac- tivities should be carried out by the community members themselves, regardless of their technical knowledge. While this ultimate goal of taking software engineers out of the loop is rather illusionary in the moment, we have targeted realizing a generic architecture based on the research methodology. It allows community members to understand their mediated actions in community information systems. In its reflec- tive conception the community information systems based on ATLAS are tightly interwoven with a set of media-centric self monitoring tools for the communities. Hence, communities can constantly measure, analyze and simulate their ongoing activities. Consequently, communities can better access and understand their com- munity need. This leads to a tighter collaboration between multimedia community information systems designers and communities. Within UMIC we have developed this complex scenario of a mobile community based on our real Bamiyan Develop- ment community, and the ATLAS/LAS approach. Virtual Campfire is an advanced scenario to create, search, and share multimedia artifacts with context awareness across communities. Hosted on the basic component the Community Engine, Virtual Campfire provides communities with a set of Context-Aware Services and Multi- media Processor Components to connect to heterogeneous data sources. Through standard protocols a large variety of (mobile) interfaces facilitate a rapid design and prototyping of context-aware multimedia community information systems. The suc- cessful realization of a couple of (mobile) applications listed as follows has proved the concept and demonstrated Virtual Campfire in practices: MIST as a multimedia based non-linear digital storytelling system; NMV as a MPEG-7 standard based multimedia tagging system; (Mobile) ACIS as a Geographic Information System (GIS) enabled multimedia information system hosting diverse user communities for the cultural heritage management in Afghanistan; and finally CAAS as a mobile application for context-aware search and retrieval of multimedia and community members based on a comprehensive context ontology modeling spatial, temporal, device and community contexts. All these applications employ the community en- gine and MPEG-7 Services within the Virtual Campfire framework. Other services and (mobile) interfaces are applied according to different communities require- ments. Virtual Campfire is running on Wireless Mesh Networks to apply high and stable network data transfer capability, and low cost, in developing countries. In order to use Web 2.0 feature, related community concepts for storytelling, a prototype called YouTell has been developed within the Virtual Campfire scenario. Figure 4 gives an overview of this new web service. Additionally to storytelling functionality an expert-finding service is integrated. Web 2.0 techniques as tagging and giving feedback contribute to a comprehensive role model for storytelling too. Tags can be analyzed for a dynamic classification of experts. This role model is also used to represent the behavior and influence of every user. In our previous research, we have focused on how to generate stories by applying the Movement Oriented Design (MOD) paradigm, which divides stories into Begin, 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 635 Fig. 4 An overview on the YouTell concepts Middle, and End parts [21]. We have designed and deployed a so-called Multimedia Integrated Story-Telling system (MIST) to create, display, and export non-linear multimedia stories [26]. MIST proves to be applied in domains of cultural heritage management well, in order to organize a great amount of multimedia content re- lated to a monument or a historical site. MIST can also be used as an e-learning application to manage multimedia learning stuff or as an e-tourism support system to generate personalized tour guide. Drawbacks or missing features are also exposed during the deployment. First of all, MIST lacks the mechanism to support users’ collaborative storytelling explic- itly. That means, more than one users are able to work on the same story together, while their activities are not recorded for each user respectively but mixed. Second, MIST can be used to create and view stories. But users can not give any personal comments to the stories. Third, it is almost impossible to search stories in the large story repositories, since these multimedia stories do not possess proper metadata to describe it content. Finally, MIST lacks authority, if a story has a serious usage e.g. learning knowledge in a certain area. The question arises, who are the experts in the storytelling communities and have more potentials to create arts? YouTell enables communities to have joint enterprises (i.e. story creation), to build a shared repertoire (i.e. stories) and to engage mutually (i.e. expert contacts). Therefore, YouTell build a platform for a community-of-practice with a number of experts [29]. YouTell has also employed the most highlighted Web 2.0 features like tagging and feedback from amateur. Hence, the conflict between experts and amateur is dealt wish in YouTell. 636 R. Klamma et al. The main design concepts as well as algorithms of the YouTell system are an appropriate role model as well as user model for storytelling, Web 2.0 tagging fea- tures, the profile-based story search algorithm, and expert finding mechanism. The Role Model All roles which should be taken into account for storytelling is specified in YouTell (cf. Figure 5). A new YouTell user John Doe gets necessary rights to execute basic features like tagging, viewing, rating and searching for a story. Experts are users which have the knowledge to help the others. There exist three different sub roles. A YouTell technician can aid users with administrative questions. A Storyteller knows how to tell a thrilling story. And finally a Maven ischaracterized as possessing good expertise. A user has to give a minimum number of good advices to the communities in order to be upgraded to an expert. Administrators have extended rights which are necessary for maintenance issues. The system admin is allowed to change system and configuration properties. Story sheriffs can delete stories and media. Additionally, there exists the user admin.He manages YouTell users and is allowed to lock or delete them. A producer create, edit and manage stories. The producer role is divided into the production leader who is responsible for the story project, the author who is responsible for the story content, the media producer who is responsible for used media, the director who is responsible for the story, and finally the handyman who is a helper for the story project. Fig. 5 The YouTell role model 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 637 The role called Bandits classifies users which want to damage the system. Ac- cording to their different behavior, they can be a troll,asmurf,ahustler or a munchkin. In contrast to bandits there exist two prestige roles: the connector and the domain lord. Whereas the connector knows many people and has a big contact network, the domain lord both has a great expertise and, at the same time, is an excellent storyteller. Web 2.0 for Storytelling: Tagging and Rating If a YouTell user wants to create a story, he first has to create a story project. With regard to his wishes he can invite other YouTell members to join his project. Ev- ery team member is assigned to at least one producer role. Every YouTell user can tag stories to describe the related content. Because the widely-in-use plain tagging approach has several disadvantages [14], a semantic tagging approach is used, too. Besides, users’ rating and viewing activities on stories are also recorded. As de- picted in Figure 6, A YouTell story are described with tags, rated by users. The popularity is also reflected by the viewing times. Profile-based Story Searching In comparison to MIST, YouTell has enhanced the story searching feature greatly. Additionally to a content based search by title or tags, a profile based search is offered to users. Figure 7 shows how the profile based story search works. In the following the corresponding algorithm is explained in detail. The set of all stories, which haven’t been seen and created by the user is described trough S DfS 1 ; :::; S n g. The function  W S 7! WLassigns a set of tags to a story, R is the set of all ratings, R S i is set set of ratings of story S i ;S i 2 S. Fig. 6 Information board of a YouTell story 638 R. Klamma et al. Fig. 7 Profile based story search algorithm Input of the algorithm is a user made tag list W Dfsw 1 ; :::sw k g. Additionally further information are necessary: the maximal result length n and the set of story ratings B of user with a similar profile. For computation of these users the Pearson r algorithm is taken(cf. [28]). Considered are user with similar or opposite ratings. If the ratings are similar the Pearson value is near to 1, if they are opposite the value is sear to -1. In the first case stories with similar ratings, in the second case stories with opposite ratings are recommended. The value has to be in a threshold L to be suitable. The Pearson value is computed with the following formula: w a;b D P m iD1 .r a;i r a /  .r b;i r b / q P m iD1 .r a;i r a / 2 P m iD1 .r b;i r b / 2 The Pearson value between user profile a and compared profile b is represented through w a;b . The variable m corresponds to the story count, i is a particular story and r its rating. The average ratings of profile a is displayed through r a . It holds B DfB S 1 ; ;B S k g with S i 2 S;1 Ä i Ä k. Furthermore B S i corresponds to the set of story ratings of user with a similar profile for story S i and finally it holds .R S i / D B S i . 1. step: Group the stories: The first group G 1 corresponds to the story set S 1 ; ;S m , S i 2 S with W  .S i / and .R S i / 2 B. The second group G 2 contains the stories S 1 ; ;S l , S j 2 S with .S j / \ W ¤;;W ª .S j / and .R S i / 2 B. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 639 2. step: 1. Take group G 1 DfS 1 ; ;S m g. a. Compute the story ratings median B S i for every story S i . b. Build a ranking corresponding to the medians 2. If jG 1 j <n, take group G 2 DfS 1 ; ;S l g. a. Be P W S 7! R a function, which assigns a number of points to every story. b. For every j; 1 Ä j Ä l it holds P.S j / D 0 c. For every tag sw i For every storyS j If sw i 2 .S j /: Compute the median m j of ratings B S j Map the result to the range [1,5]: m 0 j D m j C 3 P.S j /CDm 0 j d. Sort the stories by their score. 3. Build an overall ranking with the rankings from group 1 and group 2. This rank- ing is the output of the algorithm. Expert Finding System Users who have questions can contact an expert. A special algorithm and useful user data are necessary to determine the users knowledge, in order for the users to find the best fitting expert. For every user there exists a user profile which contains the following information: Story data are generated when a user visits or edits a story. Expert data are created with given/ received expert advices Personal data represent the user knowledge the user has acquired in the real world. These data are typed in by the user itself. With these information three tag vectors are created. They will be weighted summed up and normalized. Such a vector has the following form: 2 6 6 4 taga valuea tagb valueb tagc valuec   3 7 7 5 The final value of each tag represents the users knowledge assigned to the related tag. A value near to zero implies that the user only knows few, where as a value near 1 implies expertise at this topic. Now it will be described how the data vector is composed. First the story data vector will be created. For every story a user has visited and for every story for which 640 R. Klamma et al. the user is one of the producer, the corresponding story/media tags will be stored in a vector. The respective value is computed with the formula value DAV DV BF and – AV ODcount of appearances of a tag – DF OD date factor: The older a date, the more knowledge is lost. The value lies between 0 and 1.A1 stands for an actual date, a zero for a very old one. Four weeks correspond to a knowledge deficit of 5 percent. It holds: DF D1 .b #weeks 4 c0:05/. – BF ODrating factor: This value is computed by the explicit and implicit feedback which has been given. Then the story data vector d is computed: d D Story visit vector 0:35 C Story edit vector 0:65: After that a normalization to the range Œ0; 1 will be done: Let S Dfs 1 ; :::; s n g be the set of all tags, which occur within the set of data vectors and let v.s/ be the corresponding value. 8s 2 Sv.s/ norm D v.s/ v min v max v min and v min D minfv.s 1 /; :::; v.s n /g; v max D maxfv.s 1 /; :::; v.s n /g: In a second step the expert data vector is computed. For every expert advice a user has given/ obtained the corresponding tags are stored in a vector. The respective value will be calculated analogously to the above computation and it holds: expert data vector D advice given 0:8 Cad vice obtained 0:2: Third the personal data vector is computed. With the information the system got from the user tags and its corresponding values will be obtained. These will be taken for this vector. In a last step the final vector will be computed: data vector D 0:4 expert data vector C0:4  story data vector C0:2  personal data vector: To find an expert first a vector v Dfs 1 ; w 1 ; :::; s m ; w m g will be created with the tags the user has specified. Then this vector will be compared with all existing data vectors w 1 ; ; w n . The user with the best fitting vector will be the recommended expert. The vectors have the following form: z D .s 1 ; w 1 ;s 2 ; w 2 ; ;s m ; w m /, whereas s i is the i-th tag and w i the corre- sponding value. 1. Repeat for every vector w j ;1Ä j Ä n 2. diff j D 0 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 641 3. Repeat for every tag s i ;1Ä i Ä m of vector v 4. If s i D s j k , s j k 2 w j :diff j D diff j C .w i w j k / 5. else diff j D diff j C1 Output of this algorithm is the user for which data vector u holds: u D w j mit diff j D minfdiff 1 ; ; diff n g. Web 2.0 for the Expert-finding Algorithm How does Web 2.0 features like tagging and esp. feedback influence on expert- finding? Users can give feedback to stories and for expert advices. Feedback is very important for YouTell, because it delivers fundamental knowledge for executing the profile based search and defining the user’s expert status. Furthermore the visu- alization of feedback results (i.e. average ratings, tag clouds) help user to get an impression of the experts/story’s quality. Explicit and implicit feedback techniques are used. After visiting a story respec- tively getting an expert advice the user has the possibility to fill out a questionnaire. This explicit form of giving feedback is fundamental for YouTell. But not every user likes filling out questionnaires [31]. Therefore, also implicit feedback is employed. Although this is not as accurate as explicit feedback, it can be an effective substitute [31]. In YouTell the following user behavior will be considered: The more one user visits one story the more interesting it is. The more a story is visited by all users, the more popular it is. In addition, the integrated mailbox service offers the possibility to handle all necessary user interaction of the YouTell community. Users need to send mes- sages when they want to ask an expert, give an expert advice, invite a new team member, etc. Implementation of the YouTell Prototype An overall architecture of YouTell is illustrated in Figure 8. YouTell is realized as client/server system and is integrated in the LAS system [25] implemented in Java. The client, implemented as a web service, communicates via the HTTP protocol with the las server by invoking service methods. The LAS server handles the user management and all database interactions. New services like the expert, mailbox, YouTell user and storytelling service extend the basic LAS features and fulfill all functionality needed by YouTell. The story service extends already existing MIST features and includes methods for the management of story projects and searching for stories. The expert ser- vice contains functions for computation and management of the expert data vectors. The mailbox service manages the mailbox system. The YouTell user service extends the LAS user service and offers the possibility to add and edit user specific data. 642 R. Klamma et al. Fig. 8 System architecture of YouTell In addition, YouTell needs several different servers to work properly. The client system communicates via the HTTP protocol with an Apache tomcat server. Their Servlets and JSPs are executed for the user interface of YouTell (cf. Figure 9). In YouTell the storytelling board is integrated with Java applets which run on the client. All media of the YouTell community are stored on a FTP server. The communication with the used databases (eXist and DB2) is realized by the LAS server. [...]... Furht (ed.), Handbook of Multimedia for Digital Entertainment and Arts, DOI 10.1007/978-0-387-89024-1 30, c Springer Science+Business Media, LLC 2009 653 654 D Milam et al and what are their impressions and experiences after playing Facade? Husserl’s ¸ phenomenological philosophy [12] best suit our research question since it seeks a descriptive analysis of several individuals understanding of a phenomenon... Shardanand Social Information Filtering: Algorithms for automating word of mouth CHI 95 Proceedings, pages 210–217, 1995 29 E Wenger Communities of Practice: Learning, Meaning, and Identity Cambridge University Press, Cambridge, UK, 1998 30 E Wenger, R McDermott, and W M Snyder Cultivating Communities of Practice Harvard University Press, Boston, MA, 2002 31 R W White Implicit feedback for interactive information... Klamma, N Sharda, and M Jarke Web-based Learning with Non-Linear Multimedia Stories In W Liu, Q Li, and R W H Lau, editors, Advances in Web-Based Learning, Proceedings of ICWL 2006, Penang, Malaysia, July 19-21, volume 4181 of LNCS, pages 249–263 Springer-Verlag, 2006 27 E Stefanakis and G Kritikos The battleship “G Averof” promotion and enrichment of the museum archives In Proceedings of the XXI ISPRS... introduced a new visual form of interactive entertainment as part of interactive story systems for the participant The authoring environment also allowed characters to behave and act based on the enactment of goals The Oz architecture comprised of a simulated physical environment which contained the automated agents, a user interface, and planner Following their work, Mateas and Stern developed ABL... are commonly used today in the social sciences such as psychology, sociology, and education Husserl’s reflective examination of the structures of lived experience criticized a positivist and empiricist conception of the world as an objective universe of facts There are many branches of phenomenology by disciples of Husserl and for this study we focus on transcendental phenomenology by Moustakas [13] which... refusal of subject-object dichotomy Reality is only in the meaning of the experience of the individual According to Moustakas, the goal of phenomenological research is to provide the reader with an accurate understanding of the essential, invariant structure (or essence) of an experience 30 A Study of Interactive Narrative from User’s Perspective 659 Mallon and Webb used a focus group approach for data... interpretation of conversation breakdown and character responses They used qualitative analysis based on grounded theory where they triangulated data in the form of: observation notes, participants’ interpretations of their actions after showing them the video of their interaction with Facade, and system tracing revealing the systems’ ¸ inner interpretations of participants’ utterances [26] Some of their... understanding of the player’s lived experience A robust user model is strengthened by how it engages player behavior and is therefore much more than a mechanism to assign player actions into lists of variables and predicted outcomes 658 D Milam et al … ED: did you cheat on him grace? GRACE: Ah! TRIP: Heh, hey heh heh heh, hey, no no no, don't don't try to don't don't try to accuse me of of of. .. enjoyable qualities of this medium For our purposes, we attempt to suspend judgment in order to articulate the essence of interactive narrative from the participants’ in depth perceptions of their interactions We use a phenomenological method of data analysis to interpret the participants’ experience based on the works of Moustakas [13] and Colaizzi [14] The primary contribution of this work is in presenting... Becker, and A Feix Inscape: Storymodels for interactive storytelling and edutainment applications In International Conference on Virtual Storytelling, pages 168–171, 2005 10 M Jarke and R Klamma Reflective community information systems In Y M et al., editor, Proceedings of the International Conference on Enterprise Information Systems (ICEIS 2006), LNBIP, pages 17–28, 2008 11 C Kelleher, R Pausch, and S . (  ), and R. Wakkary School of Ineractive Arts and Technology, Simon Fraser University, Surrey, BC, Canada e-mail: fdma35, magy, rwakkaryg@sfu.ca B. Furht (ed.), Handbook of Multimedia for Digital. existing MIST features and includes methods for the management of story projects and searching for stories. The expert ser- vice contains functions for computation and management of the expert data. sharing a success for any kind of professional com- munity, independent of size or domain of interest, a generic community engine for Social Software is needed. After some years of experience, with