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Identifying Temporal Patterns and Key Players in Document Collections

Identifying Temporal Patterns and Key Players in Document Collections

This video was recorded at Machine Learning Summer School (MLSS), Taipei 2006. We consider the problem of analyzing the development of a document collection over time without requiring meaningful citation data. Given a collection of timestamped documents, we formulate and explore the following two questions. First, what are the main topics and how do these topics develop over time? Second, to gain insight into the dynamics driving this development, what are the documents and who are the authors that are most influential in this process? Unlike prior work in citation analysis, we propose methods addressing these questions without requiring the availability of citation data. The methods use only the text of the documents as input. Consequentially, they are applicable to a much wider range of document collections (email, blogs, etc.), most of which lack meaningful citation data. We evaluate our methods on the proceedings of the Neural Information Processing Systems (NIPS) conference. Even with the preliminary methods that we implemented, the results show that the methods are effective and that addressing the questions based on the text alone is feasible. In fact, the text-based methods sometimes even identify influential papers that are missed by citation analysis.

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