Description and Indexing of Editorial Cartoons

While access to images in general has improved in the last 20 years, due to both advances in electronic storage and dissemination and to improvements in the intellectual provisions of them, access to editorial cartoons lags behind access to other types of images. While there have been piecemeal or ad hoc efforts to organize large cartoon collections, these efforts have been based on the wants and needs of the organizers, publishers, or collectors. The purpose of this research was to gather information about user's descriptions of editorial cartoons. Specifically, it gathered terms and phrases provided by users to describe a set of editorial cartoons, both in an image tagging environment and in a simulated query environment. The population for this research was a blended sample; one population consisted of academics in fields that were assumed to have an interest in the research itself, and who were seen as likely to give a full, rich description of each image. The second population consisted of non-degree holding participants, against which the first results could be compared. The images used in this study were political cartoons from the five most recent Pulitzer Prize-winning editorial cartoonists. Content analysis of the cartoons' descriptions placed each description into one of Jörgensen's 12 Classes of image description, and the frequencies of each Class in this study were compared to similar studies. The results of this research show that while editorial cartoons can be described using Jörgensen's 12 Classes, they are described in very different ways than are other images. It was found that the Class ABSTRACT CONCEPTS was far more dominant when describing and searching for editorial cartoons than was so for other types of images; the Class LITERAL OBJECT was dominated by the attribute Text in both scenarios; VIEWER REACTIONS play a far larger role for these images than for others; and four Classes that are at least somewhat useful in searching for other types of images were almost unused when searching for editorial cartoons. Demographic variables show major differences in behavior among those of different education levels in tagging, and among different political views and genders when querying. Confirmatory interviews with image professionals and editorial cartoonists showed that the results would be of some use when implemented in the field. The results of this research would help inform efforts to index any image where the meaning of it was more important than the image content, and may help to describe all types of non-textual records of history and commentary.