A tag cloud (also known as a word cloud or weighted list in visual design) is a visual representation of text data which is often used to depict keyword metadata on websites, or to visualize free form text. Tags are usually single words, and the importance of each tag is shown with font size or color. When used as website navigation aids, the terms are hyperlinked to items associated with the tag.
History
In the language of visual design, a tag cloud (or word cloud) is one kind of "weighted list", as commonly used on geographic maps to represent the relative size of cities in terms of relative typeface size. An early printed example of a weighted list of English keywords was the "subconscious files" in Douglas Coupland's Microserfs (1995). A German appearance occurred in 1992.
The specific visual form and common use of the term "tag cloud" rose to prominence in the first decade of the 21st century as a widespread feature of early Web 2.0 websites and blogs, used primarily to visualize the frequency distribution of keyword metadata that describe website content, and as a navigation aid.
The first tag clouds on a high-profile website were on the photo sharing site Flickr, created by Flickr co-founder and interaction designer Stewart Butterfield in 2004. That implementation was based on Jim Flanagan's Search Referral Zeitgeist, a visualization of Web site referrers. Tag clouds were also popularized around the same time by Del.icio.us and Technorati, among others.
Oversaturation of the tag cloud method and ambivalence about its utility as a web-navigation tool led to a decline of usage among these early adopters. Flickr gave a five-word acceptance speech for the 2006 "Best Practices" Webby Award, which simply stated "sorry about the tag clouds."
A second generation of software development discovered a wider diversity of uses for tag clouds as a basic visualization method for text data. Several extensions of tag clouds have been proposed in this context.
Types
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.
Frequency
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.
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.
Significance
Instead of frequency, the size can be used to represent the significance of words and word co-occurrences, compared to a background corpus (for example, compared to all the text in Wikipedia). This approach cannot be used standalone, but it relies on comparing the document frequencies to expected distributions.
Categorization
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.
There are some approaches to construct tag clusters instead of tag clouds, e.g., by applying tag co-occurrences in documents.
More generally, the same visual technique can be used to display non-tag data, as in a word cloud or a data cloud.
The term keyword cloud is sometimes used as a search engine marketing (SEM) term that refers to a group of keywords that are relevant to a specific website. In recent years tag clouds have gained popularity because of their role in search engine optimization of Web pages as well as supporting the user in navigating the content in an information system efficiently. Tag clouds as a navigational tool make the resources of a website more connected, when crawled by a search engine spider, which may improve the site's search engine rank. From a user interface perspective they are often used to summarize search results to support the user in finding content in a particular information system more quickly.