Flickr Image Tagging : Patterns Made Visible

Joan Beaudoin is a doctoral student at Drexel University. She can be reached by email at jeg56drexel.edu. T he development and subsequent popularity of image tagging at sites such as Flickr (www.flickr.org) has been a phenomenon to receive considerable attention over the course of the last few years. After spending more than a decade cataloging and providing access to images in an academic setting I, too, felt compelled to take a look at what this Web 2.0 image-sharing site had to offer. There were two interconnected ideas at play when I began thinking about performing a study of Flickr tagging. The first of these ideas had to do with looking for an underlying pattern for the image tags. It seemed likely that some commonalities would occur among what at first glance appeared to be the chaos of personally applied image tags. Finding these common patterns among the tags would clarify what types of information people typically associate with their images. The second idea concerned the effectiveness of image tagging. If patterns were discovered among the image tags, I believed these could be used to alleviate some of the problems associated with tagging. So early in 2006 with these two ideas fresh in mind, I carried out a small study of the image tags used at Flickr.com. To conduct the study I gathered the top 10 image tags of 14 randomly chosen Flickr users and downloaded them through the site’s open APIs. In order to discern if there were patterns to be found in the application of the image tags, I applied conceptual labels to each of the 140 image tags. This labeling process was iterative, and after several passes through the entire set of image tags a model consisting of 18 categories (Table 1) emerged. During this process it became apparent that image tags could have multiple meanings and as a result, I allowed some to be assigned to several categories. Thus, a tag such as cross could simultaneously be considered a verb, a thing, an emotion and an adjective. Model Evaluation In order to evaluate the usefulness of the model to represent the various image tags, I gave the files of the extracted Flickr image tags and the categories with definitions to four people, who then categorized the image tags in a way that made most sense to them. Their categorizations of the image tags were combined with mine and the occurrences were then tallied to determine the overall category agreement (Table 2) and patterns of category usage (Table 3).