md` * **Font property - Size**

This is the most commonly used channel in word clouds. It maps magnitude to the height of the font. However, the literature on visualization suggests that font size might not be an optimal channel to convey quantitative information because of the irregular shape of words and the non-direct relationship between height, width and painting area. * **Font property - Luminance**

According to [(Bateman et al. 2008)](https://dl.acm.org/citation.cfm?id=1379130), after discarding color hue that only works for categorical values and font weight that has too little resolution to be effective, color intensity (contrast or saturation) is the next best font channel to convey importance of a keyword and support quantitative information. Depending on the color model used (HSL or HSV, for instance) and fixing the color font to a default black, we can map strong levels of attention to a font with small luminance (pure black) and small levels of attention to light grey. * **Additional Mark - Bars Length**

Bars are one of the most used marks in visualization, they allow the use of very effective and precise channels like position and length. When bars are aligned they can clearly encode the value using position, the strongest channel for quantitative visualization. Like [(Felix et al. 2017)](https://ieeexplore.ieee.org/abstract/document/8017641), we use horizontal bars, since they align with the word reading order. One issue, though, is that in the original article they did not explain how to calculate the range of the bar's length, so we propose to use the length of the longer word as a maximum attention equal to 1, and the other bar lengths linearly proportional. Another option to explore is aligning the words in columns to improve the comparison of attention between words, as the result of the study by Felix suggests. * **Additional Mark - Circle Area**

Circles are often used in scatter plots as bubbles to convey a third value. Also in other visualizations in which the spatial properties are already used to encode other information (e.g., bubble maps), circles are often used. Since the diameter of circles change together with area size, it is not possible to find a unique strategy to overlap circles with words the same way we do with bars. For this reason, in our proposal, words are placed to the right side of each circle. * **Aditional Mark - Background Color Intensity**

This is the traditional way to present attention, as in the work by [Yang et. el (2016)](https://www.aclweb.org/anthology/N16-1174). Mapping directly attention to font color can hide the words if they receive too low attention, so this encoding use background color intensity to highlight the words with more attention. However, for certain tasks the direct encoding of attention by font color could be more effective, such as identifying only the top ${tex `n` } words used to classify a document. `