Telling Stories about Dynamic Networks with Graph Comics

International audienceIn this paper, we explore graph comics as a medium to communicate changes in dynamic networks. While previous research has focused on visualizing dynamic networks for data exploration, we want to see if we can take advantage of the visual expressiveness and familiarity of comics to present and explain temporal changes in networks to an audience. To understand the potential of comics as a storytelling medium, we first created a variety of comics during a 3 month structured design process, involving domain experts from public education and neuroscience. This process led to the definition of 8 design factors for creating graph comics and propose design solutions for each. Results from a qualitative study suggest that a general audience is quickly able understand complex temporal changes through graph comics, provided with minimal textual annotations and no training

Storytelling and Visualization: An Extended Survey

Throughout history, storytelling has been an effective way of conveying information and knowledge. In the field of visualization, storytelling is rapidly gaining momentum and evolving cutting-edge techniques that enhance understanding. Many communities have commented on the importance of storytelling in data visualization. Storytellers tend to be integrating complex visualizations into their narratives in growing numbers. In this paper, we present a survey of storytelling literature in visualization and present an overview of the common and important elements in storytelling visualization. We also describe the challenges in this field as well as a novel classification of the literature on storytelling in visualization. Our classification scheme highlights the open and unsolved problems in this field as well as the more mature storytelling sub-fields. The benefits offer a concise overview and a starting point into this rapidly evolving research trend and provide a deeper understanding of this topic

TimeBender: Interactive Authoring of 3D Space-Time Narratives

Communication of scientific results and discoveries to, for example, fellow domain experts, business partners, students, or the general public, is an important part of research. Communication through visualization has been proven to be effective when the representations are memorable and engaging, and research has shown that these communicative visualizations can be further enhanced with narratives for certain audiences. A challenge faced by scientists is to create memorable and engaging visualizations for communication which traditionally has been done by trained illustrators and designers. Therefore, we created TimeBender, a framework and prototype implementation to bridge this gap specifically for authoring narrative posters in a 3D environment with a space-time dimension. The posters feature multiple scenes forming the narrative, which are connected by an elongated object encoding the narrative flow. We demonstrate that our approach is capable of aiding the authoring of these posters through a 3-step pipeline where, first, the scenes are set up individually, then, the global layout of scenes in the poster space is determined, before details, such as textual elements, are added. TimeBender supports animation as each scene is rendered dynamically within the poster. The framework and example results were evaluated in an expert interview with a professional illustrator.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

BlueRedMagenta: effective implementation of color, image, animation, and interaction in an original web-based sequencial art narrative

This project is an attempt to review and explore the strengths and limitations of the technologies and tools in the web-based sequential art medium in order to find a process with which one can create more efficiency in delivering impactful sequential images to convey a narrative. The information gathered and analyzed here is then used to inform creative decisions in forming the final work, which is a web-based original sequential art narrative. The final web-based narrative endeavors to function as a ‘proof of concept’ for the topics discussed and analyzed

DATA-DRIVEN STORYTELLING FOR CASUAL USERS

Today’s overwhelming volume of data has made effective analysis virtually inaccessible for the general public. The emerging practice of data-driven storytelling is addressing this by framing data using familiar mechanisms such as slideshows, videos, and comics to make even highly complex phenomena understandable. However, current data stories still do not utilize the full potential of the storytelling domain. One reason for this is that current data-driven storytelling practice does not leverage the full repertoire of media that can be used for storytelling, such as speech, e-learning, and video games. In this dissertation, we propose a taxonomy focused specifically on media types for the purpose of widening the purview of data-driven storytelling by putting more tools in the hands of designers. We expand the idea of data-driven storytelling into the group of casual users, who are the consumers of information and non-professionals with limited time, skills, and motivation , to bridge the data gap between the advanced data analytics tools and everyday internet users. To prove the effectiveness and the wide acceptance of our taxonomy and data-driven storytelling among the casual users, we have collected examples for data-driven storytelling by finding, reviewing, and classifying ninety-one examples. Using our taxonomy as a generative tool, we also explored two novel storytelling mechanisms, including live-streaming analytics videos—DataTV—and sequential art (comics) that dynamically incorporates visual representations—Data Comics. Meanwhile, we widened the genres we explored to fill the gaps in the literature. We also evaluated Data Comics and DataTV with user studies and expert reviews. The results show that Data Comics facilitates data-driven storytelling in terms of inviting reading, aiding memory, and viewing as a story. The results also show that an integrated system as DataTV encourages authors to create and present data stories

MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning

We introduce MoviePuzzle, a novel challenge that targets visual narrative reasoning and holistic movie understanding. Despite the notable progress that has been witnessed in the realm of video understanding, most prior works fail to present tasks and models to address holistic video understanding and the innate visual narrative structures existing in long-form videos. To tackle this quandary, we put forth MoviePuzzle task that amplifies the temporal feature learning and structure learning of video models by reshuffling the shot, frame, and clip layers of movie segments in the presence of video-dialogue information. We start by establishing a carefully refined dataset based on MovieNet by dissecting movies into hierarchical layers and randomly permuting the orders. Besides benchmarking the MoviePuzzle with prior arts on movie understanding, we devise a Hierarchical Contrastive Movie Clustering (HCMC) model that considers the underlying structure and visual semantic orders for movie reordering. Specifically, through a pairwise and contrastive learning approach, we train models to predict the correct order of each layer. This equips them with the knack for deciphering the visual narrative structure of movies and handling the disorder lurking in video data. Experiments show that our approach outperforms existing state-of-the-art methods on the \MoviePuzzle benchmark, underscoring its efficacy