Analyzing the generalizability of the network-based topic emergence identification method

Tracking #: 2689-3903

This paper is currently under review
Authors: 
Sukhwan Jung
Aviv Segev

Responsible editor: 
Guest Editors DeepL4KGs 2021

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Full Paper
Abstract: 
The field of topic evolution helps the understanding of the current research topics and their histories by automatically modeling and detecting the set of shared research fields in the academic papers as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, where the topics are defined as the relationships of its neighborhoods in the past, allowing the result to be extrapolated to the future topics. Twenty fields-of-study keywords were selected from the Microsoft Academic Graph dataset, each representing a specific topic within a hierarchical research field. The binary classification for newly introduced topics from the years 2000 to 2019 consistently resulted in accuracy and F1 over 0.91 for all twenty datasets, which is retained with one-third of the 15 features used in the experiment. Incremental learning resulted in a slight performance improvement, indicating there is an underlying pattern to the neighbors of new topics. The result showed the network-based new topic prediction can be applied to various research domains with different research patterns.
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