This paper explores how AI-driven storytelling can transform news articles into fictional narratives using structured retelling techniques. We introduce NewsReteller, a system that explores the generative capabilities of Large Language Models to create stories from news content through three distinct approaches: genre-based storytelling, which adapts narratives to established literary styles; structured storytelling, which reshapes events using predefined biased schemes (story skeletons); and data-driven storytelling, which emphasizes factual clarity and analytical framing. To assess the system’s ability to reinterpret factual content, we generated multiple stories from a single news article using each of these approaches. The results illustrate how different retelling strategies influence narrative framing, thematic emphasis, and information presentation, highlighting the potential of our method to generate creative reinterpretations of real-world events.
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Narrative structures such as the Hero’s Journey and Heroine’s Journey have long influenced how characters, themes, and roles are portrayed in storytelling. When used to guide narrative generation in systems powered by Large Language Models (LLMs), these structures may interact with model-internal biases, reinforcing traditional gender norms. This workshop examines how protagonist gender and narrative structure shape storytelling outcomes in LLM-based storytelling systems. Through hands-on experiments and guided analysis, participants will explore gender representation in LLM-generated stories, perform counterfactual modifications, and evaluate how narrative interpretations shift when character gender is altered. The workshop aims to foster interdisciplinary collaborations, inspire novel methodologies, and advance research on fair and inclusive AI-driven storytelling in games and interactive media.
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The report from Inholland University is dedicated to the impacts of data-driven practices on non-journalistic media production and creative industries. It explores trends, showcases advancements, and highlights opportunities and threats in this dynamic landscape. Examining various stakeholders' perspectives provides actionable insights for navigating challenges and leveraging opportunities. Through curated showcases and analyses, the report underscores the transformative potential of data-driven work while addressing concerns such as copyright issues and AI's role in replacing human artists. The findings culminate in a comprehensive overview that guides informed decision-making in the creative industry.
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This article investigates gender bias in narratives generated by Large Language Models (LLMs) through a two-phase study. Building on our existing work in narrative generation, we employ a structured methodology to analyze the influence of protagonist gender on both the generation and classification of fictional stories. In Phase 1, factual narratives were generated using six LLMs, guided by predefined narrative structures (Hero's Journey and Heroine's Journey). Gender bias was quantified through specialized metrics and statistical analyses, revealing significant disparities in protagonist gender distribution and associations with narrative archetypes. In Phase 2, counterfactual narratives were constructed by altering the protagonists’ genders while preserving all other narrative elements. These narratives were then classified by the same LLMs to assess how gender influences their interpretation of narrative structures. Results indicate that LLMs exhibit difficulty in disentangling the protagonist's gender from the narrative structure, often using gender as a heuristic to classify stories. Male protagonists in emotionally driven narratives were frequently misclassified as following the Heroine's Journey, while female protagonists in logic-driven conflicts were misclassified as adhering to the Hero's Journey. These findings provide empirical evidence of embedded gender biases in LLM-generated narratives, highlighting the need for bias mitigation strategies in AI-driven storytelling to promote diversity and inclusivity in computational narrative generation.
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This report provides the global community of hospitality professionals with critical insights into emerging trends and developments, with a particular focus on the future of business travel. Business travellers play a pivotal role within the tourism industry, contributing significantly to international travel, GDP, and business revenues.In light of recent disruptions and evolving challenges, this forward-looking study aims not only to reflect on the past but, more importantly, to anticipate future developments and uncertainties in the realm of business travel. By doing so, it offers strategic insights to help hospitality leaders navigate the ever-evolving landscape of the industry.Key findings from the Yearly Outlook include:• Recovery of International Travel: By 2024, international travel arrivals have surpassed 2019 levels by 2%, signalling a full recovery in the sector. In Amsterdam, there was a 13% decrease in business traveller numbers, offset by an increase in the average length of stay from 2.34 to 2.71 days. Notably, more business travellers opted for 3-star accommodations, marking a shift in preferences.• Future of Business Travel: The report outlines a baseline scenario that predicts a sustainable, personalised, and seamless business travel experience by 2035. This future will likely be driven by AI integration, shifts in travel patterns—such as an increase in short-haul trips, longer stays combining business and leisure—and a growing focus on sustainability.• Potential Disruptors: The study also analyses several potential disruptors to these trends. These include socio-political shifts that could reverse sustainability efforts, risks associated with AI-assisted travel, the decline of less attractive business destinations, and the impact of global geopolitical tensions.The Yearly Outlook provides practical recommendations for hospitality professionals and tourism policymakers. These recommendations focus on building resilience, anticipating changes in business travel preferences, leveraging AI and technological advancements, and promoting sustainable practices within the industry.
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Player behavioural modelling has grown from a means to improve the playing strength of computer programs that play classic games (e.g., chess), to a means for impacting the player experience and satisfaction in video games, as well as in cross-domain applications such as interactive storytelling. In this context, player behavioural modelling is concerned with two goals, namely (1) providing an interesting or effective game AI on the basis of player models and (2) creating a basis for game developers to personalise gameplay as a whole, and creating new user-driven game mechanics. In this article, we provide an overview of player behavioural modelling for video games by detailing four distinct approaches, namely (1) modelling player actions, (2) modelling player tactics, (3) modelling player strategies, and (4) player profiling. We conclude the article with an analysis on the applicability of the approaches for the domain of video games.
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This deaf-led work critically explores Deaf Tech, challenging conventional understandings of technologies ‘for’ deaf people as merely assistive and accessible, since these understandings are predominantly embedded in medical and audist ideologies. By employing participatory speculative workshops, deaf participants from different European countries envisioned technologies on Eyeth - a mythical planet inhabited by deaf people - centered on their perspectives and curiosities. The results present a series of alternative socio-technical narratives that illustrate qualitative aspects of technologies desired by deaf people. This study advocates for expanding the scope of deaf technological landscapes, emphasizing the needs of establishing deaf-centered HCI, including the development of methods and concepts that truly prioritize deaf experiences in the design of technologies intended for their use.
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Big data analytics received much attention in the last decade and is viewed as one of the next most important strategic resources for organizations. Yet, the role of employees' data literacy seems to be neglected in current literature. The aim of this study is twofold: (1) it develops data literacy as an organization competency by identifying its dimensions and measurement, and (2) it examines the relationship between data literacy and governmental performance (internal and external). Using data from a survey of 120 Dutch governmental agencies, the proposed model was tested using PLS-SEM. The results empirically support the suggested theoretical framework and corresponding measurement instrument. The results partially support the relationship of data literacy with performance as a significant effect of data literacy on internal performance. However, counter-intuitively, this significant effect is not found in relation to external performance.
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Immersive journalism (IJ) is often assumed to be inherently emotion-inducing. Through using inclusive technology, interaction possibilities and immersive narratives, the audience should ideally experience what feels like to be in a certain situation. However, for the most part we do not know to which extent and in what form IJ influences the experience of emotions. We wanted to investigate, whether, and if so, which characteristics of IJ are related to the experience of emotions, and which role the personality trait empathy tendency plays in this respect. This is important, as the evaluation of IJ often relies on the emotion-inducing assumption thereof. Four different experiments comparing one immersive journalistic characteristic (level of inclusion, interaction possibilities, immersive narratives) to the respective non-immersive counterpart were conducted. Results indicate that while the level of inclusion and interaction possibility increase the intensity of the experience, the immersive narrative influences the valence dimension of emotions. Additionally, empathy tendency is found to be a relevant moderator for these effects. Conclusions are threefold. First, the narrative form of IJ is key; second, the analysis of IJ needs to go beyond the level of inclusion; third, including emotions when assessing IJ is fundamental to understand its impact.
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