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| Frontier Detection in Interdisciplinary Research from the Perspective of Altmetrics: Taking Medical Informatics as an Example |
| Wang Feifei, Liu Ming |
| School of Economics and Management, Beijing University of Technology, Beijing 100124 |
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Abstract This paper combines the data of Altmetrics based on online data with the traditional method of frontier detection to compensate for the shortcomings of the traditional leading edge detection method. To this end, this study used the Web of Science database and the Altmetric.com platform to obtain traditional metrics and data of Altmetrics. Five evaluation indicators were constructed for the frontier detection of the study—immediacy, growth, scientific influence, social attention, and intersectionality. Specifically, 55 research topics were extracted from experimental data using the LDA algorithm, and the frontier detection indicator score for each topic was calculated. Next, the principal component analysis method, the entropy weight method, and the gray correlation degree method were used to comprehensively evaluate the five evaluation indicators for each topic. By calculating the Kendall’s Coefficient of Concordance of the three evaluation methods, it can be verified that the evaluation method results are consistent and the results are acceptable. Finally, four topics were selected as cutting-edge topics with better frontier properties according to the skyline method. The experimental results show that the introduction of Altmetrics data can supplement the traditional frontier detection method, and the final extracted result is ideal, which is in line with the actual development needs.
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Received: 12 July 2019
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