User Assessment of a Visual Web Genre Classifier

Users assess the “appropriateness” of web documents in many ways. Traditionally, appropriateness has been solely a matter of relevance to a particular topic. But users are concerned with other aspects of document “genre”, such as the level of expertise assumed by the author, or the amount of detail. In previous work, we have used machine learning to automatically classify documents along a variety of genre dimensions, and we have developed a graphical interface that depicts documents visually along orthogonal genre dimensions. In order to validate the design of our interface, we have performed two experiments – a brainstorming session and a web-based survey which have shown that users perceive genre dimensions as independent. In the present paper we elaborate in more detail the idea behind the classifier and draw upon the possibility of user “firstglance” biases in assessment of web documents.