Understanding Data Through Character-Oriented Storytelling

When sharing a story based on data, the author has a goal they want to communicate. This could aim to persuade, educate, inform, or entertain the audience. Along with this goal, the story itself needs to be easy to understand, enjoyable, and maintain scientific accuracy. While many methods exist for creating and sharing a story with data, there is room to improve how we think about and create the visuals that tell that story.

Stories become engaging through Characters; characters often make a story more enjoyable and memorable and help the audience follow along until the end. By analyzing 160 existing data stories, we identify key features of characters in these narratives and how they relate to the larger concept of "character-oriented design." We highlight the roles and visual styles of characters in data stories and how these characters relate to each other.

We recognize the importance of having a clear central character that the audience can connect with to follow the narrative. We present "character-oriented design" by illustrating how to create data characters based on common data story Plots. This work introduces a framework for data characters and extends the data storytelling process using character-oriented design.

#Stages of Character-Oriented Visual Storytelling

Character-oriented visual storytelling includes three stages: identification, organization, and presentation. A key part of the organization stage is defining the main characters, supporting characters, and antagonists and their relationships within the story.

Data can be complex and sometimes abstract, which can make it hard to share information clearly. Visualization helps to make complex information accessible and understandable. Through visuals, we can present complicated datasets, highlight hidden insights, and share findings with a wider audience.

Data storytelling has roots going back to our ancestors, who used storytelling to describe the location of food sources or warn against dangers. Today, storytelling remains a powerful tool for engagement, understanding, and conveying cause and effect.

Data storytellers aim to share the fascinating insights found in data with others. In our stories, we strive to communicate scientific insights while captivating the audience with the narrative. The challenge is balancing content that is easy to consume and enjoyable while maintaining scientific integrity. This balance can sometimes lead to struggles for the audience to grasp insights or see relevance.

#Data Storytelling Process

The storytelling process can be divided into three stages: identification, organization, and presentation. The first stage involves gathering story pieces, which are often insights from data analysts or domain experts. The author and analysts work together to determine the intention they want to convey. This intention shapes the story's direction.

Next, in the organization stage, various narrative frameworks can help arrange these events into a cohesive plot. This involves understanding the relationships among story pieces and deciding on their order. The result should be a structured outline that clearly conveys the message.

Finally, the presentation stage involves giving the story its visual style and appeal. Here, many techniques can be used to tailor the story for the intended audience. It is in this stage that we can broaden our view on how to design the visuals that play out the story.

#Bringing Stories to Life with Data Characters

In our work, we look at data-driven, visual storytelling, especially focusing on the characters that make these stories vivid. As data storytellers, we want to create experiences that leave a lasting impact. Characters breathe life into stories, making them more captivating and easier to remember.

In other media, characters serve as a gateway for the audience to enter new and often complex worlds. For example, in "Star Wars: Episode IV - A New Hope," Luke Skywalker guides the audience through the Star Wars universe, allowing them to learn about it through his actions rather than a list of terms.

By applying characters to convey scientific insights, we can enhance the power of data storytelling. Our work seeks to answer questions about what a data character is, how we classify them, and how we can develop them to fit within a story.

Through our analysis of 160 data stories, we present a framework for data characters, identifying three main roles: main characters, supporting characters, and antagonists. We also look into how conflicts are framed within data storytelling, as these conflicts drive narratives and can elevate how stories are told.

Notably, we found that having a clear, identifiable central character can help the author align the story elements with their intentions, create a consistent message, and effectively reach the audience. The main ideas of our contributions include a framework for data characters that extends to the broader data storytelling process, a summary of storytelling terms from various literature, and examples from different types of data stories to showcase the utility of our design concepts.

#Understanding Storytelling

Storytelling has always been a part of human culture, leading to the development of rules and structures for effective narratives. In exploring data storytelling, we draw on components from both written and visual media.

In "Aspects of the Novel" by E. M. Forster, he outlines common elements found in English novels: story, characters, plot, fantasy, prophecy, pattern, and rhythm. He defines a story as "a narrative of events arranged in their time sequence," while the plot emphasizes cause and effect.

While written and visual media often allow for flexibility in storytelling, data stories are usually based on concrete facts or non-fiction, leading to less freedom in narrative construction. Still, common themes emerge for both storytelling and data storytelling, such as characters, plots, themes, settings, and conflicts. The goal of many data stories is to reach a broad audience through visually presented findings or messages.

The role of a storyteller is to summarize narrative events so that the audience perceives them as a cohesive story.

#Key Elements of a Story

We define a set of storytelling terms to ensure clarity in data storytelling. However, even within data stories, interpretations of terms like through-line, plot, and character vary. Therefore, we provide deeper explanations of these concepts.

#Through-Line

The storytelling process requires understanding what the author wants to communicate to the audience. Authors aim for the audience to feel something and leave with a key message. This message is often called the theme of a story or a recurring idea. A story may have various themes, but the through-line is the main focus that runs throughout the narrative and helps maintain the story's direction.

#Plot

A plot describes a series of events with a purpose. Each event is causally connected and essential to the overall story. Every element should contribute to a single emotional impact, which ties back to the through-line.

#Character

A character is an entity that affects itself or others. A story typically follows what a character desires, how they pursue that desire, and what they must sacrifice to achieve it. The drive behind a character's actions is their "desire," and as they seek their goals, they often learn more about them. Characters may encounter struggles that can change them or solidify their traits. The storyteller's task is to show a character's development or the reasons they might not change.

#The Process of Data Storytelling

In the field of visualization, there is extensive work that addresses storytelling with data. Data storytelling and narrative visualization are two key areas. Recent literature categorizes this work into three groups: who is involved (authoring tools and audience), how stories are narrated (narratives and transitions), and why storytelling is effective (memorability and interpretation). Our work aims to enhance the storytelling approach in data by examining and identifying key features of characters in these narratives.

The general storytelling process involves both the plot and the characters. Little attention has been given to how to design data characters, as much focus has been placed on creating and conveying the story plot.

#Relationship Between the Storyteller and the Story

We categorize data storytelling into three stages: identification, organization, and presentation. At the beginning of story development, the data storyteller may not know what to share. Gathering a set of events or story pieces, such as data facts or insights, helps the storyteller decide what to include.

Simply having these pieces does not create a narrative. The relationships among them, like cause and effect, allow the storyteller to organize these pieces into a plot for presentation. If the storyteller is uncertain about the relationships or the subject matter's relevance, the audience may feel similarly.

In the organization stage, data storytellers select story pieces, arrange them into a plot, and create an outline for the story. Several frameworks can help guide this process based on narrative structure and communication goals.

#Narrative Structures

For data storytelling, there are popular narrative structures like drill-down, martini glass, and interactive slideshow that have been widely discussed. These structures offer a way to structure stories based on the movement and pacing of the narrative, depending on audience interaction.

#Communicative Goals

The communicative goal often informs the design choices a storyteller makes. Understanding the relevance of visual elements to the story's theme helps the storyteller identify critical story pieces. Irrelevant information could create multiple sub-plots, which may confuse the audience and detract from their interest in the story.

This motivates our inquiry into how visual elements can be represented as data characters. Characterizing story pieces as data characters can reinforce the through-line and help organize the story, making it easier for storytellers to deliver clear messages to their audience.

#Identifiable Central Character

In visual data storytelling, a central character should be identifiable to guide the audience. Our analysis shows that this character's depiction can range from abstract representations to actual people. For instance, visual encodings like lines to show temperature or tree rings to represent immigration can serve as characters.

Characters in data stories have been explored in prior research focused on character-driven storytelling. While earlier works emphasize characters as human or virtual agents, data stories often use abstract representations. Our perspective is that abstract visuals can still function as data characters.

#Effective Data Characters

Effective data characters can guide the audience's understanding throughout the data story. We explore how characters can frame abstractions and share insights. Data characters should have clear links to the story's themes and advance the plot.

Designing data characters requires consideration of their properties and how they effectively communicate scientific content. We offer a framework for classifying data characters, their roles, and behaviors.

#Roles of Data Characters

Character roles in storytelling influence how narratives unfold. In simple terms, there are three primary roles: main characters, supporting characters, and antagonists.

#Main Character

The main character (MC) is crucial and needs to be visually present in the story. It serves as a focal point for the audience to understand what is happening. The visual representation of the MC can vary widely, but it should be central to the message.

#Supporting Characters

Supporting characters (SCs) enhance the depth of the MC and help fulfill its desires. These characters might provide context, extra information, or alternative data representations without detracting from the MC's journey.

#Antagonistic Character or Force

The antagonist presents challenges to the MC. This can be a visible character or an unseen force that creates conflict. Antagonists can represent external misconceptions, misunderstandings, or obstacles that the MC must confront.

#Recognizing Conflict and Tension

Understanding data characters leads to structuring story content and clarifying the narrative's purpose. Conflict is a means to convey motivation and engage the audience. There are two types of conflict: internal and external.

#Internal Conflict

An internal conflict occurs when a character deals with its desires or beliefs. It shapes character development and illustrates struggles within the character.

#External Conflict

External conflict arises when a character faces challenges from outside forces, such as an antagonist. In many data stories, external forces often challenge the MC, creating tension throughout the narrative.

#Analyzing a Data Comic Example

To illustrate data characters and conflicts, we analyze a data comic. The MC in this story represents a concept, while supporting characters add context. The antagonistic force introduces challenges, driving the narrative forward.

#Character Web

In our character web, we map how main characters, supporting characters, and antagonists interact. Main characters pursue desires while antagonistic forces challenge these aspirations, creating a dynamic narrative landscape.

#Story Plots and Data Characters

We identify several common plots for data stories, including refuting claims, revealing unintended consequences, and tracking changes in systems. Each plot can utilize characters differently, establishing a link between the characters and the story structure.

#Character-Oriented Design Space

From our analysis of data characters, we propose a character-oriented design space that allows storytellers to develop and apply characters in data narratives. The design space emphasizes understanding the characters' roles and their significance.

#Motivating Data Characters

Developing a data character involves defining its narrative goal and desire. A clear desire motivates the character throughout the story, connecting it to the larger message.

#Challenges and Premises

As storytellers craft characters, they need to address unique challenges related to the content, audience comprehension, and complexity of the scientific information being presented. Establishing a clear through-line helps keep the story focused and engaging.

#Creating Data Characters

A data character offers a lens into complex concepts and can be defined based on attributes and behaviors related to the story's aim. Multiple characters may be needed to represent different aspects of a single concept.

#Character Roles in Story Plots

We explore how various character roles might be expressed within the context of five types of data story plots, including refuting claims and revealing information of personal interest.

#Conclusion and Future Directions

Our work emphasizes the importance of characters in data storytelling, offering a framework for understanding their roles and relationships. Recognizing characters helps storytellers craft engaging narratives that maintain scientific integrity. We encourage further exploration into character roles and how they function in data stories, with a focus on enhancing storytelling effectiveness.

By considering characters in the development of data stories, we can improve how these narratives resonate with audiences and foster a deeper understanding of the data presented.