Exploring narrativity in data visualization in journalism

Many news stories are based on data visualization, and storytelling with data has become a buzzword in journalism. But what exactly does storytelling with data mean? When does a data visualization tell a story? And what are narrative constituents in data visualization? This chapter first defines the key terms in this context: story, narrative, narrativity, showing and telling. Then, it sheds light on the various forms of narrativity in data visualization and, based on a corpus analysis of 73 data visualizations, describes the basic visual elements that constitute narrativity: the instance of a narrator, sequentiality, temporal dimension, and tellability. The paper concludes that understanding how data are transformed into visual stories is key to understanding how facts are shaped and communicated in society

Evaluation of storytelling in information visualization (MPhil to PhD Transfer Report)

Story telling has been used throughout the ages as a means of communication between people and to convey and transmit knowledge from one person to another, and from one generation to the next. In various domains, formulating of messages, ideas, or findings into a story has proven its efficiency in making them understandable, comprehensible, memorable, interesting, and engaging. Information Visualization as an academic field has also utilised the power of storytelling to make visualizations more understandable and interesting for a variety of audiences, including experts. However, although storytelling has been a hot topic in information visualization for some time, little or no empirical evaluations exist to compare different approaches of storytelling through information visualization. There is also a need for work that addresses in depth some particular criteria and techniques of storytelling such as transitions types in visual stories in general and data-driven stories in particular. A within subject experiment with 13 participants has been conducted to explore empirically how two different models of story delivery with information visualization influence narratives/stories constructed by audiences. Specifically, the first model involves direct narrative by a speaker using a visualization design to tell a story, while the second model involves constructing a story by interactively exploring visualization software. An openended questionnaire in controlled laboratory settings has been used in which the primary goal was to collect a number of stories derived from the two models. All the stories written by the participants were transcribed, analysed, and coded, using data-driven and preset themes. Themes included initial perception of the main story pattern/topic, insight types derived, narrative structures, and unexpected type of insights gained. This experiment was followed by a semi-structured interview where each participant answered two Likert-scale questions on each delivery model, and commented on the overall experiment. It is found that although most participants found telling a story easier with the first model (narrative) they did not perform better in other aspects. The second model (software) was advantegeous in the variety of insight types gained and participants accepted the message and information more neutrally. In contrast, participants were more critical about the data in software model than in the narrative model. The role of time in structuring story events was more apparent in the software model. These findings have some significant practical implications on storytelling through information visualization. A statement of the work done and a work plan for the remaining period of the PhD is also included explaining the proposed enhancement to the experiment conducted and further research work planned to address the issue of transitions in storytelling visualization

The Effect of Information Visualization Delivery on Narrative Construction and Development

We conducted a between-subject experiment with 32 participants to explore how two different models of information visualization delivery influence narratives constructed by audiences. The first model involves direct narrative by a speaker using visualization software to tell a data story, while the second model involves constructing a story by interactively exploring the visualization software. We used an open-ended questionnaire in a controlled laboratory settings in which the primary goal was to collect a number of written data stories derived from the two models. The participants’ data stories and answers were all analysed and coded using a number of themes, including insight types, and narrative structures. Our findings show that while the delivery model does not significantly affect how easy or difficult the participants found telling a data story to be, it does have an effect on the tendency to identify and use outliers insights in the data story if they are not distracted from this by direct narration, and on the narrative structure and depth of the data story. Our approach to data analysis and different storytelling axes can be usefully applied to other studies and comparisons of storytelling approaches

Sea of Genes: Combining Animation and Narrative Strategies to Visualize Metagenomic Data for Museums

We examine the application of narrative strategies to present a complex and unfamiliar metagenomics dataset to the public in a science museum. Our dataset contains information about microbial gene expressions that scientists use to infer the behavior of microbes. This exhibit had three goals: to inform (the) public about microbes' behavior, cycles, and patterns; to link their behavior to the concept of gene expression; and to highlight scientists' use of gene expression data to understand the role of microbes. To address these three goals, we created a visualization with three narrative layers, each layer corresponding to a goal. This study presented us with an opportunity to assess existing frameworks for narrative visualization in a naturalistic setting. We present three successive rounds of design and evaluation of our attempts to engage visitors with complex data through narrative visualization. We highlight our design choices and their underlying rationale based on extant theories. We conclude that a central animation based on a curated dataset could successfully achieve our first goal, i.e., to communicate the aggregate behavior and interactions of microbes. We failed to achieve our second goal and had limited success with the third goal. Overall, this study highlights the challenges of telling multi-layered stories and the need for new frameworks for communicating layered stories in public settings.Comment: This manuscript has been accepted to VIS 2020 and TVCG 9 pages 2 reference

The effect of information visualization delivery on narrative construction and development

We conducted a within-subject experiment involving 13 participants that empirically explore how two different models of story delivery involving information visualization influence audience-constructed narratives. The first model involves a speaker using visualization software to communicate a direct narrative, while the second involves constructing a story by interactively exploring visualization software. We used an openended questionnaire in controlled laboratory settings, with the primary goal of collecting a number of stories derived from the two models, followed by two Likert-scale questions on the ease of telling and curiosity about the story in each delivery model. We qualitatively analysed the stories constructed by the participants, based on a number of themes tied to storytelling, including time and place and narrative structure. The study’s results reveal some interesting possible differences in how users receive, interpret, and create stories in each case

Narrative Visualization: Sharing Insights into Complex Data

This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation

Character-Oriented Design for Visual Data Storytelling

When telling a data story, an author has an intention they seek to convey to an audience. This intention can be of many forms such as to persuade, to educate, to inform, or even to entertain. In addition to expressing their intention, the story plot must balance being consumable and enjoyable while preserving scientific integrity. In data stories, numerous methods have been identified for constructing and presenting a plot. However, there is an opportunity to expand how we think and create the visual elements that present the story. Stories are brought to life by characters; often they are what make a story captivating, enjoyable, memorable, and facilitate following the plot until the end. Through the analysis of 160 existing data stories, we systematically investigate and identify distinguishable features of characters in data stories, and we illustrate how they feed into the broader concept of "character-oriented design". We identify the roles and visual representations data characters assume as well as the types of relationships these roles have with one another. We identify characteristics of antagonists as well as define conflict in data stories. We find the need for an identifiable central character that the audience latches on to in order to follow the narrative and identify their visual representations. We then illustrate "character-oriented design" by showing how to develop data characters with common data story plots. With this work, we present a framework for data characters derived from our analysis; we then offer our extension to the data storytelling process using character-oriented design. To access our supplemental materials please visit https://chaorientdesignds.github.io/Comment: Accepted to TVCG & VIS 2023 Pre-Print. Storytelling, Data Stories, Explanatory, Narrative visualization, Visual metapho

A visual language to characterise transitions in narrative visualization

We use a taxonomy of panel-to-panel transitions in comics, refined the definition of its components to reflect the nature of data-stories in information visualization, and then, use the taxonomy in coding a number of VAST challenges videos from the last four years. We represent the use of transitions in each video graphically with a diagram that shows how the information was added incrementally in order to tell a story that answers a particular question. A number of issues have been taken into account when coding transitions in each video as well as in designing and creating the visual diagram such as, nested transitions, the use of sub-topics, and delayed transitions

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis