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| Recent Advancements in Detection and Evolutionary Tracking of Scientific Topics: A Multi-perspective Survey and Prospect |
| Cen Yonghua1,2, Wang Yuefen1,2 |
1.Management School, Tianjin Normal University, Tianjin 300387
2.Institute for Big Data Science, Tianjin Normal University, Tianjin 300387 |
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Abstract Leveraging instructive approaches to comprehensively and accurately detect and track topics from the big data of historical literature is a hot and cutting-edge issue that has garnered substantial interest from different disciplines, especially scientometrics in information science. The major underlying mechanisms involve four perspectives, which include frequency, content, citation, and synthesized analyses. This study attempts to review the latest literature published in prestigious international and domestic journals, to update the main progress of relevant perspectives in scientific topic detection and evolution analysis by unravelling their essential implementation paths. In particular, it considers the risks inherited in existing perspectives, including the biases in heterogeneous importance of knowledge units or network relationships, temporal decay of knowledge, trap of the small samples of emergent topics, dilemma in fitting the natural development and evolution of topics, and failure in portraying knowledge flow and evolution in micro level. In response to these issues, the study focuses attention to the future trend of a synthesized perspective.
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Received: 18 April 2022
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