Different ordering, interactivity, and messaging, can offer readers a different reading experience. The visual narrative genres, together with interactivity and messaging, must balance the narrative intended by the author with story discovery on the part of the reader. A narrative visualization will normally be placed along a spectrum of author-driven and reader-driven approaches. Most visualizations lie between the two extremes. Author-driven stories are usually linear in order. They are heavy in messaging and have no interactivity. The purpose of author-driven visualization is for communication, so it aims at clarity and speed. In contrast, reader-driven visualization is for explorations and for query. They have no prescribed ordering, limited messaging, but more interactivity. A reader-driven approach supports tasks such as data diagnostics, pattern discovery, and hypothesis formations. In the past, many visualizations fell into the author-driven or reader-driven dichotomy. However, visualizations are increasingly striking a balance between the two approaches, providing room for limited interactivity within the context of a more structured narrative. Here, we discuss three common schemas. Martini Glass structures start with an author-driven approach with a default view of the visualization, and allow the readers to explore the data freely once the intended narrative is complete. The New York Times budget forecasts is an example of a martini glass narrative. It presents US budget surpluses and deficits over time alongside with the predictions from each presidential administration. A stepper widget encourages readers to move through a story in a linear fashion. Slides one to four determine the annotations and the graphs shown. But by the fifth slide, the readers are encouraged to interact with the slider. In this style of storytelling-- beginning as a linear author-driven narrative, but then opening up for reader-driven exploration-- the narrative change from author to reader resembles the shape of a martini glass. Another common schema is interactive slideshow. The visualization is presented as a regular slideshow where the user is able to interact with certain points of the presentation before allowing the story to advance to the next stage. Drill-down story is based on a reader-driven approach where a general theme is presented and the user can interact with particular points of the visualization to reveal additional details and background information relevant to the main theme. Besides, a narrative theme were proposed by Siegel and Hear. Many researchers also advocate to integrate techniques for emotional appeals to narrative visualization. Data designers are finding ways to deploy digital features to arouse users emotionally, creating excitement, and enhancing user engagement and understanding. There are three ways to achieve emotional appeals. The designers are aiming to provide an engaging, humanizing, and personalizing experience to readers. To engage audiences, our graphic needs to offer sensory stimulation. The first technique to stimulate the senses is the use of color. Color can evoke an emotional response. Psychologically, we respond immediately to the difference between warm and cool colors. Warm colors cause excitement or anxiety, and the cool colors are often more calming. Second, instead of using common bar charts or pie charts, the use of novel forms of graphics can evoke surprise and excitement. However, designers must beware of assuming that the audience can easily interpret a novel chart. Another powerful way to engage the audience is to tell a narrative through animations that change with space or time. This dynamic form of storytelling brings out a sense of immediacy and can evoke various emotions as the information is animated. One example is Gapminder. The visualization is a scatterplot of countries telling the story of our collective health and wealth through 215 years. As the chart animates through time, the country circles move along the coordinates that represent life expectancy and income per person. Watching this story unfold through the animation encourages a variety of emotions, such as curiosity and excitement as the overall situation for humanity improves. Second, efforts to humanize data helps the audience connect the data to what they are about. When the audience is able to see the humanity behind the data, they are more likely to care about what is being visualized. One way to do this is to zoom in on one dot. When highlighting one data point, which usually represents one individual, designers can insert the story of that individual in the visualization. This adds qualitative information to a quantitative display and connects the human to the data and enables the viewer to see the individual. While zooming in on one dot dives into the story of one individual, showing the mass as individuals draw attention to every individual in the data. The Out of Sight, Out of Mind visualization humanizes data by showing the mass as individuals. The visualization calls attention to the deaths of innocent civilians from US drone strikes in Pakistan. The bar chart is made up of ticks for each human life lost, enabling each casualty to be represented individually. The bars are colored to categorize deaths with children deaths in bright red and high-profile deaths with white color. On the victims tab, a monthly view shows the counts as horizontal collections of wee people, where each human figure represents a victim. The final humanizing technique is about putting a face to the data. This technique is implemented in Faces of the Dead by The New York Times, an interactive graphic that displays photos of fallen US service members in Iraq or Afghanistan. Each pixel that makes up the image represents an individual in the dataset. By selecting a pixel, one portrait image of a service member and his or her data is presented. Personalizing the data emphasizes the close proximity between the data and the viewer. Visualizing data that is close in time or connects to the audience's space triggers a strong emotional response. Particularly, when a user is able to interact with or explore a visualization, their interests begin to dominate their visual experience. Like interactive graphics, such as "how birth year inferences political view" and "Can you live on the minimum wage?"" By The New York Times, allows a user to select their birth year or enter their personal financial data so they can illustrate and highlight data that is relevant to them. The components of data, visuals, and narrative are what we're looking for in journalistic visualizations. Visualization is powerful, but even more powerful is the ability to connect visuals and tell a story with data.
