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| Burst Term Detection Study Based on Multi-Indicators |
| Peng Guochao, Kong Yongxin, Wang Yuwen |
| School of Information Management, Sun Yat-sen University, Guangzhou 510006 |
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Abstract Since burst terms are forward-looking and informative, burst term detection (BTD) helps to predict research fronts and hotspots in certain subject areas. In this study, we build a multi-indicator system for BTD, including burst indicators (“random,” “growth,” and “burst”), knowledge fusion indicators, and influence indicators. Based on “random” and “growth,” three categories of burst terms are clustered by K-means, namely, emergence terms, strong burst terms, and weak burst terms. Combining the burst indicators, knowledge fusion indicators, and influence indicators, the burst terms with different developmental statuses were identified. The results show that emergence terms with a high burst can gain more attention and have more influence at the initial stage. Burst terms with a high degree of knowledge fusion indicate that the breadth and intensity of fusion are higher, and they are more likely to develop into research hotspots in the future. Finally, burst terms with high influence indicates that they receive wide attention and have a certain research base and have a higher probability to become a research frontier in the future.
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Received: 26 July 2021
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