Viegas, F.B., Wattenberg, M. & J. Feinberg (2009). “Participatory visualizations with Wordle.” IEEE Transactions on Visualization and Computer Graphics, 15(6), 1137-1144.
In this article, Viegas et al. (2009) introduce “Wordles,” their distinctions among similar data visualizations, and methodology to discover certain characteristics of Wordle users and their wider community.
Wordles represent a popular form of tag clouds, a common data visualization generally used to represent word frequency in text, with more frequent words represented in bigger and less frequent in smaller font. However, there are some key differences between an average tag cloud and a Wordle, in both their calculation and final appearance. In a Wordle, text size and word frequency are represented linearly; that is, the size of a word increases the same amount for each time it appears. Often, tag clouds calculate word size by utilizing the square root instead. Additionally, the Wordle algorithm allows words to appear in any free space not occupied by text–for example, in the space of an “o” or rotated vertically along the side of an “l.” The authors note that these changes were made for aesthetic reasons; however, particularly regarding how text size is calculated, the side effect may be a more straightforward relationship between size and frequency.
The authors also speak to their expectations of the Wordle community as casual infovis and a participatory culture. Casual infovis refers to situations or communities where lay users depict information in a personally meaningful way. Participatory culture refers to the tenor of conversation between the generator of information (or Wordles) and their audience; this very commonly occurs on the Internet, in the form of website user feedback, fan fiction, or comment boards on news stories or blog posts, to name a few examples.
“Wordles in the wild”: Methods and results
Because Wordle does not collect demographic information for users, who can make and download a graphic without logging in or creating an account, Wordle has little data to describe their users beyond the Wordles they create. To learn more about the wider community of Wordle users, the authors use a dual approach: Research into “Wordles in the wild,” an Internet search of previously created graphics and how they have been used online; and a survey of current visitors to the Wordle site.
“Wordles in the wild” (pp. 1139) were initially identified through Google search. The authors examined the first 500 sites returned for “Wordle,” and used these “prominent” (pp. 1139) examples to guide more specific research. Through this process, the authors identified several major categories for both Wordle users and how Wordle graphics are used, the largest being “education.” While a rather ingenious way to collect context, in the face of little circumstantial data to understand how Wordles have been used, snowball research does yield very little control over both the completeness and quality of found data.
Wordle also placed a survey link on its homepage, asking users to provide feedback about themselves and their graphics. The survey was first piloted for two days, and following feedback and revisions reposted for one week; the authors do not note specifically what feedback was given, or how the survey changed. During the week it was live, the survey received about 4,300 responses, which (assuming one Wordle per user per day, with no user overlap) represents a response rate of about 11%; although the authors note a margin of error of about 1%, they also recognize that given difficulties controlling for demographic variables and self-selection bias, the results should only be viewed as “a general guide” (pp. 1140).
The authors do admit a significant selection bias in this data, among both “wild Wordles” and survey respondents; they do not delve deeply into demographic data, beyond sex, age and occupation.
Do Wordles even count as a data visualization?
Given the authors’ results, there is little question that Wordle users clearly represent a participatory culture. They outline several ways that users collaborate with not only their data, but also their audience. As one example of professional use: Journalists, particularly during the 2008 presidential election, used Wordle to illuminate trends from political text and speeches. There are also many examples of personal or “fun” uses given, particularly focusing upon Wordles as gifts–for baby showers, church groups, and so on.
The authors, however, do note that the categorization of the Wordle community as “casual infovis” does not clearly convey some of the Wordle community’s more interesting characteristics. For example, “casual” doesn’t quite express the personal connection many users expressed toward their Wordle text; over half indicated that had written it themselves. Also, not all users identify their graphics or the use thereof, analytical or otherwise, as personally meaningful.
Besides the characteristics of Wordle users, the strong focus upon creating Wordles rather than using them as an analytical tool demonstrates to the authors that Wordles are not being utilized as intended, or perhaps as expected. Particularly considering the large number of survey respondents who did not understand the significance of word size within a graphic, does this then disqualify Wordles from truly being data visualizations?
This may be true in the wider community of users–particularly when considering the Wordles created as Valentine’s Day cards for spouses, or as bridal gifts and birthday presents. Wordles as gifts, or Wordles created for fun seem commonly to not have an analytical context. However, I would argue that within education, Wordle is working as intended, plus some. Educators create Wordles of new vocabulary words or Shakespearan sonnets to illuminate classroom discussion; students likewise are asked to participate in creating new Wordle graphics as an assignment or classroom activity. Bandeen and Sawain (2012) outline several concrete applications for Wordles in class, including (broadly):
- Understanding major concepts
- Identifying and defining unfamiliar terms
- Connecting current passages with previous readings
- Pointing out unexpected words
- Identifying missing words
- Theorizing connections among words
which pull from all levels of the Bloom’s taxonomy. In addition to serving as an analytical tool to guide discussion, Wordles (or tag clouds in general) are used collaboratively to explore texts in unique or unusual ways not always apparent at first read. Whether students are creating or viewing Wordle graphics, and whether or not the graphics are used in strictly an “analytical” sense, they are actively engaging the material in a meaningful way–both as casual infovis and a participatory culture.
Sources
Bandeen, H.M. & Sawain, J.E. (2012). Encourage students to read through the use of data visualizations. College Teaching, 60, 38-39.