People as an AI computing tool
The tagging phenomenon started about 3 years ago, with people putting labels on their posts in their blogs or on their pictures or videos. Now, a researcher from the University of Southern California (USC) has discovered the newest AI computing tool: people. Computer scientist Kristina Lerman thinks that 'she has found a new source of artificial intelligence computing power to solve difficult IT problems of information classification, reliability, and meaning.' For example, 'by extracting the tags that Flickr users had described the images with, and applyng a mathematical technique called the "Expectation-maximization (EM) algorithm,' she found it's possible to quite accurately separate pictures of insects from pictures of cars returned by the "beetle" search. So can we expect better search tools in the future? Time will tell.
Kristina Lerman works at the USC's Information Sciences Institute and she says that "extracting 'metadata' about transactions -- who is talking to whom, who is listening, how conclusions are reached, and how they spread -- can help researchers answer currently refractory problems about documents: their accuracy and quality, their categorization, the relation of their embedded terminology." For about a decade, she notes, researchers sought a way to organize data so that someone searching for a specific kind of "check" wouldn't have to weed out unwanted references to chess, symbols, verification procedures, financial documents, political science theories and many more.
But now, people are putting tags on everything they put on the Web, photos, videos, or simple posts. For an example on this, please read this story from 2004, "How Do You Use del.icio.us?" But things have changed and grown since 2004.
"New social websites aimed at sharing information such as del.icio.us and Flickr organically grow ways for site members to access each others holdings. Typically, the members themselves spontaneously create a tagging system, encouraged by the site architecture. The tags emerging from such systems, Lerman and collaborators have found, can be turned to broader purposes."
Lerman gives more explanations in several technical papers. Let's start with "Personalizing Image Search Results on Flickr" (April 12, 2007). Here are two links to the abstract and to the full paper (PDF format, 11 pages). Below is the introduction.
"The social media site Flickr allows users to upload their photos, annotate them with tags, submit them to groups, and also to form social networks by adding other users as contacts. Flickr offers multiple ways of browsing or searching it. One option is tag search, which returns all images tagged with a specific keyword. If the keyword is ambiguous, e.g., ``beetle'' could mean an insect or a car, tag search results will include many images that are not relevant to the sense the user had in mind when executing the query. We claim that users express their photography interests through the metadata they add in the form of contacts and image annotations. We show how to exploit this metadata to personalize search results for the user, thereby improving search performance."
In this second paper, "Exploiting Social Annotation for Automatic Resource Discovery" (April 12, 2007), Lerman writes that "information integration applications, such as mediators or mashups, that require access to information resources currently rely on users manually discovering and integrating them in the application. Manual resource discovery is a slow process, requiring the user to sift through results obtained via keyword-based search. Although search methods have advanced to include evidence from document contents, its metadata and the contents and link structure of the referring pages, they still do not adequately cover information sources -- often called ``the hidden Web''-- that dynamically generate documents in response to a query. The recently popular social bookmarking sites, which allow users to annotate and share metadata about various information sources, provide rich evidence for resource discovery." Here ar the links to the abstract and to the full paper (PDF format, 7 pages).
Finally, in "Social Information Processing in Social News Aggregation" (March 15, 2007), she wrote that "The rise of the social media sites, such as blogs, wikis, Digg and Flickr among others, underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media has lead to a new paradigm for interacting with information, what we call 'social information processing'. In this paper, we study how social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show, by tracking stories over time, that social networks play an important role in document recommendation. The second contribution of this paper consists of two mathematical models. The first model describes how collaborative rating and promotion of stories emerges from the independent decisions made by many users. The second model describes how a user's influence, the number of promoted stories and the user's social network, changes in time." Here are the links to the abstract and to the full paper (PDF format, 20 pages).
With all these references, I think you'll have a busy weekend.
Sources: University of Southern California news release, June 27, 2007; and various websites
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