Identification and Prediction of Emerging Topics through Their Relationships to Existing Topics

Abstract

Understanding the current research topics and their histories allow researchers to focus their capabilities on the current research trends. The field of topic evolution helps the understanding by automatically model and detect the set of shared research fields in the academic papers as topics. The authors propose a novel topic evolution method for identifying and predicting the emergence of new topics under the assumption that neighborhoods of new topics in the future have distinguishable structural features. Eight journals were selected from the Microsoft Academic Graph dataset, each representing topics networks with varying size, history, and research domains. Both retrospective classification and prospective prediction showed promising performance with classifications above 0.89 for six journals and coefficients of determination exceeding 0.95 for five journals. The result showed both the retrospective identification and the prospective prediction can be done, validating the assumption that topic evolution events can be predicted with a network-based approach.

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