Scientific articles are the major mechanism for researchers to report their results, and a collection of papers on a discipline can reveal a lot about its evolution, such as the emergence of new topics. Nonetheless, given a broad collection of papers it is typically very difficult to grasp important information that could help readers to globally interpret, navigate and then focus on the relevant items for their task. Content-based document maps are visual representations created from evaluating the (dis)similarity amongst the documents, and have been shown to support exploratory tasks in this scenario. Documents are represented by visual markers placed in the 2D space so that documents close share similar content. Albeit the maps allow visually identifying groups of related documents and frontiers between groups, they do not explicitly convey the temporal evolution of a collection. We propose a technique for creating content-based similarity maps of document collections that highlight temporal changes along time. Our solution constructs a sequence of maps from time-stamped sub-sets of the data. It adopts a cumulative backwards strategy to preserve user context across successive time-stamps, i.e., maps do not change drastically from one time stamp to the next, favouring user perception of changes.
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