A tag cloud or word cloud (or weighted list in visual design) is a visual depiction of user-generated tags, or simply the word content of a site, typically used to describe the content of web sites. Tags are usually single words and are normally listed alphabetically, and the importance of a tag is shown with font size or color. Thus, both finding a tag by alphabet and by popularity are possible. The tags are usually hyperlinks that lead to a collection of items that are associated with a tag.
The first use of tag clouds on a high-profile website was on the photo sharing site Flickr, created by Flickr co-founder and interaction designer Stewart Butterfield. That implementation was based on Jim Flanagan's Search Referral Zeitgeist, a visualization of Web site referrers. Tag clouds have also been popularized by Del.icio.us and Technorati, among others. Flickr would later apologize to the web-development community in their five-word acceptance speech for the 2006 "Best Practices" Webby Award, where they simply stated "sorry about the tag clouds."
The first published appearance of a tag cloud (or at least a weighted list) in the English language may have been as the "subconscious files" in Douglas Coupland's Microserfs (1995); a German appearance occurred at least three years earlier.
Prior to weighted list representation of tag clouds, paper maps had used the concept of weighted font size and font weights to represent relative size or importance of towns and cities. On 24 March 2009, CNN created what they claimed was the "largest word cloud in the free world" for that night's Anderson Cooper 360°. It was a word cloud of President Obama's address to the press earlier that day.
In recent years tag clouds gained even more popularity because of their role in search engine optimization of web pages. Properly implemented tag clouds make the website appear to search engine spiders more interlinked which tends to improve its search engine rank.
There are three main types of tag cloud applications in social software, distinguished by their meaning rather than appearance. In the first type, there is a tag for the frequency of each item, whereas in the second type, there are global tag clouds where the frequencies are aggregated over all items and users. In the third type, the cloud contains categories, with size indicating number of subcategories.
In the first type, size represents the number of times that tag has been applied to a single item. This is useful as a means of displaying metadata about an item that has been democratically 'voted' on and where precise results are not desired. Examples of such use include Last.fm (to indicate genres attributed to bands) and LibraryThing (to indicate tags attributed to a book).
In the second, more commonly used type, size represents the number of items to which a tag has been applied, as a presentation of each tag's popularity. Examples of this type of tag cloud are used on the image-hosting service Flickr, blog aggregator Technorati and on Google search results with DeeperWeb .
In the third type, tags are used as a categorization method for content items. Tags are represented in a cloud where larger tags represent the quantity of content items in that category.
More generally, the same visual technique can be used to display non-tag data, as in a word cloud or a data cloud.
Tag clouds are typically represented using inline HTML elements. The tags can appear in alphabetical order, in a random order, they can be sorted by weight, and so on. Most popular is a rectangular tag arrangement with alphabetical sorting in a sequential line-by-line layout. The decision for an optimal layout should be driven by the expected user goals. Some prefer to cluster the tags semantically so that similar tags will appear near each other. Heuristics can be used to reduce the size of the tag cloud whether or not the purpose is to cluster the tags.
A data cloud or cloud data is a data display which uses font size and/or color to indicate numerical values It is similar to a tag cloud but instead of word count, displays data such as population or stock market prices.
A text cloud or word cloud is a visualization of word frequency in a given text as a weighted list. The technique has recently been popularly used to visualize the topical content of political speeches.
Extending the principles of a text cloud, a collocate cloud provides a more focused view of a document or corpus. Instead of summarising an entire document, the collocate cloud examines the usage of a particular word. The resulting cloud contains the words which are often used in conjunction with the search word. These collocates are formatted to show frequency (as size) as well as collocational strength (as brightness). This provides interactive ways to browse and explore language.
Tag clouds have been subject of investigation in several usability studies. The following summary is based on an overview of research results given by Lohmann et al.:
In principle, the font size of a tag in a tag cloud is determined by its incidence. For a word cloud of categories like weblogs, the frequency of use for example, corresponds to the number of weblog entries that are assigned to a category. For small frequencies it's sufficient to indicate directly for any number from one to a maximum font size. For larger values, a scaling should be made. In a linear normalization, the weight ti of a descriptor is mapped to a size scale of 1 through f, where tmin and tmax are specifying the range of available weights.
for ti > tmin; else si = 1
Implementations of tag clouds also include text parsing and filtering out unhelpful tags such as common words, numbers, and punctuation.