1. Colors and icons

Our information nutrition labels are represented by simple, easy-to-identify and well-known icons, which have been shown to be easily understood by users (Antunez et al., 2013; Campos et al., 2011; Hersey et al., 2013; Roberto and Khandpur, 2014).Moreover, previous work reports that simple additional texts allow for a quicker processing of information represented by an icon (Campos et al., 2011). Therefore the information nutrition label is shown additionally as text.

To make nutrition labels more comprehensible, colors indicating amounts of nutrients are helpful (Aschemann-Witzel et al., 2013; Crosetto et al., 2016; Ducrot et al., 2016). Relevant research reports that both traffic light colors and monochromic colors work equally well (Aschemann-Witzel et al., 2013). Traffic light colors are most common with red indicating high (i.e. negative) and green indicating low (i.e. positive) levels of nutrients (Kim et al., 2018). Since we do not want to bias the users towards reading an article or not, but rather give information about its content, we chose to use different shades of blue in our plugin. A light blue indicates low and a dark blue indicates high levels of a certain label. Additionally, blue stands for trust, honesty and security (Venngage, 2018), which should indicate that the user is operating a reliable tool.

When deciding on what charts and figures to use, we again took into account that simple and commonly known visualizations are easiest to comprehend (Campos et al., 2011). Thus, we chose plain bar charts for representing overall nutrition label scores as well as scores of sub-labels. Additionally, we enriched it with percentages as well as coloring. Consequently, the amount of a label contained in a news article is visualized in an understandable and easy-to-process way. For lettering, the font Futura is used. It is a modern, straightforward and clean typeface often used in state-of-the-art websites and fits the simple and genuine layout.

  1. Positioning and information distribution

Following the so-called gestalt laws of grouping, objects that are closer to each other (law of proximity) are perceived as belonging together.


Figure 1: NewsScan plugin

Moreover, to indicate the grouping of information, separations between those groups are useful (law of continuity). Therefore, the distinct labels are separated from each other by horizontal and vertical lines. To obviate information overload (Eppler and Mengis, 2004) when using NewsScan, we reduced the information on the landing page to a minimum. Only the icons with their respective overall score are visible on the front side of the cards we used for visualization (as shown in Figure 1 for article popularity, ease of reading, sentiment, objectivity and political bias). Therefore, the users can get a first impression about different nutrition labels of the article. When time is scarce, a simple visualization where users can find the demanded information easily is most practicable (Crosetto et al., 2016). However, if users are interested in getting more detailed information, we created a backside for each card. The backside also shows the total score and, if available, relevant sub-labels that are used to calculate the overall scores (see Figure 1: source popularity). Additionally, on hovering over the names of the labels and sub-labels, the user gets a short explanation about what the wording and score mean. To further avoid possible confusion on the user side, all of the labels are represented in the same way. Overall and sub-label scores are mapped to a range from 0-100% and icons, texts and charts are arranged consistently.