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| Detection of Hotspot in Scientific Fields Based on Emerging Pattern Analysis of Social Q&A Community Contents |
| Yu Jing |
| Department of Politics, East China Normal University, Shanghai 200241 |
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Abstract Hot spot identification of scientific fields is one of the key research issues in the fields of science and technology intelligence and bibliometrics. It can constitute a reference and basis for policy-making of science and technology or education departments or research decision-making for researchers. Methods of hot spot identification in existing studies are primarily based on bibliometrics methods, without using abundant Web data. This study proposes a hot spot identification framework based on Emerging Pattern Recognition, which uses the question and answer content of Social Q&A Community to identify research hotspots in a field. First, keywords in the question and answer contents are extracted and clustered based on their co-occurrence. Second, a set of candidate hotspot patterns is constructed based on the clustering results, and emerging pattern recognition method is used to identify hotspots and analyze their trends. Experiments based on the dataset from the “Machine Learning” topic of zhihu.com were analyzed using the chi-square test and compared with frontier research. The results indicated that this framework can effectively identify research hotspots. This framework has strong realizability in that it alleviates the high computational complexity problem of the Emerging Pattern Recognition method by using keywords clustering. Moreover, it has potential application value in online community hotspot identification and other related issues.
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Received: 10 January 2020
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