Analyzing The Perceptions Of Change In A Distributed Collection Of Web Documents

It is not unusual for documents on the Web to degrade and suffer from problems associated with unexpected change. in an analysis of the Association for Computing Machinery conference list, we found that categorizing the degree of change affecting digital documents over time is a difficult task. More specifically, we found that categorizing this degree of change is not a binary problem where documents are either unchanged or they have changed so dramatically that they do not tit within the scope of the collection. it is in part, a characterization of the intent of the change. In this paper, we present a case study that compares change detection methods based on machine learning algorithms against the assessment made by human subjects in a user study. Consequently, this paper will focus on two research questions. First, how can we categorize the various degrees of change that documents endure? And second, how did our automatic detection methods fare against the human assessment of change in the ACM conference list?