For decades, digital technologies have been used to offer engaging experiences in cultural heritage sites, and these experiences have shown useful as a means to convey the histories associated with the sites. While some experiences are information-driven (e.g., tour guides), others are story-driven and therefore need a narrative justification for why the past and the present collide and in which way. This paper explores how narrative conventions from genre fiction – specifically, ghost stories, science fiction, historical fiction and fantasy – can frame narrative content in cultural heritage sites. We discuss these specific literary genres and their use in locative games to achieve the temporal collisions necessary for framing story-driven locative cultural heritage experiences. We identify the nature of the main temporal connections used by the different genres (including issues of narrative control) and offered a tentative mapping of the temporal connections onto an actual cultural heritage site.

Many existing Virtual Reality (VR) applications in the Digital Cultural Heritage (DCH) domain are for education purposes.As educational VR DCH experiences become more prevalent, it becomes increasingly important to understand the user and learner experience of such installations.This work reports on a user study (n=30) evaluating three educational VR DCH experiences using three existing User experience (UX) evaluation methodologies from related fields and three learning evaluation methodologies.A total of 31 participants were recruited for the experiment, resulting in a dataset of 30 valid records.Our research seeks to explore the relationship between UX and Learning experience (LX), and their impact on learning in VR DCH experiences.Our results suggest that UX and LX in educational VR DCH experiences can influence certain aspects of learning, such as retention, concentration, motivation, and flexibility.Additionally, specific aspects of the educational VR DCH experience captured evidence by three existing UX evaluation and three learning evaluation methodologies are identified.These include instrumental aspects (ease of use, learnability, efficiency, etc.), stimulation of new experiences, the role of interactions, immersion in VR DCH contexts and flexibility of learning pace and using learning materials.

In this paper, we present an approach for generating dialogues for characters within the context of computational narratives \nusing personality-based features for deep neural networks. The approach integrates the requirements of both narrative genres and personality traits for the definition of character-based stylistic models. \nThe modelling of characters’ features from existing datasets of complete stories permits the generation of personality-rich character dialogues. We present early results from an evaluation based on a sample of characters’ personality traits across different narrative genres, \ndemonstrating variability in the resulting dialogues

Narratives form a key component of multimedia knowledge representation on the Web. However, many existing multimedia narrative systems either ignore the narrative qualities of any media, or focus on the literal depicted content ignoring any subtext. Ignoring narrative subtext can lead to erroneous search results, or automatically remixed content that lacks cohesion. We suggest that subtext can be computationally modeled in terms of Tomashevsky’s hierarchy of themes and motifs. These elements can then be used in a semiotic term expansion algorithm, incorporating knowledge of subtext into search and subsequent narrative generation. We present two experimental applications of this technique. In the first, we use our thematic model in the automatic construction of photo montages from Flickr, comparing it to more traditional term expansion based on co-occurrence, and showing that this improves the perceived relevance of images within the montage. In the second, we use the thematic model in order to automatically identify Flickr images to illustrate short stories, where it dampened the perception of unwanted themes (an effect we describe as reducing thematic noise). Our work is among the first in this space, and shows that thematic subtext can be tackled computationally.