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| Dynamic Identification of Emerging Topics in Discipline Based on the Comparison between Different Types of Media: A Method Combining Altmetrics and Citations |
| Duan Qingfeng, Yan Xuxian, Chen Hong, Liu Dongxia |
| School of Management Science & Engineering, Shanxi University of Finance and Economics, Taiyuan 030006 |
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Abstract The timeliness advantage of social networking media helps in identifying emerging scientific topics. However, the evolution of emerging topics in disciplines is relatively different between social media and publishing media in that their altmetrics tend to dominate citations during the period of emergence. We believe that the fast-growing gap occurring between these two indicators is a key basis for distinguishing scientific emerging topics from other topics. As such, we propose a method to recognize the underlying emergent topics in disciplines via comparison among different media. First, by combining altmetrics and citations, we devise the “rgap” indicator (a gap indicating the relative differences of media in terms of the activeness of topics) to conduct sequential comparisons. Second, we employ the Burst Detection Algorithm to detect the burst status of topics, using the sequence of the “rgap” indicator, which can help identify the emerging topics and show their process of evolution. Finally, we conduct an empirical analysis in the field of information science, and the empirical analysis has proven that this identification method is effective and reliable. The identification method based on a media comparison index showed good identification ability and timeliness advantage. We also found that a satisfactory result can be achieved to some extent when using altmetrics indicators that are characterized by high level of coverage and prevalence, such as tweets or posts.
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Received: 15 May 2021
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