期刊
JOURNAL OF INFORMETRICS
卷 17, 期 3, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.joi.2023.101422
关键词
Topic influence; PageRank; Co-word network; Social media data
This study developed an effective topic ranking model, 4EFRRank, which takes into account the reinforcing effect of academic entities on topic influence. Experimental results show that the 4ER-Rank model successfully combines classic co-word metrics and effectively reflects high citation topics.
Several studies have reported on metrics for measuring the influence of scientific topics from different perspectives; however, current ranking methods ignore the reinforcing effect of other academic entities on topic influence. In this paper, we developed an effective topic ranking model, 4EFRRank, by modeling the influence transfer mechanism among all academic entities in a com-plex academic network using a four-layer network design that incorporates the strengthening effect of multiple entities on topic influence. The PageRank algorithm is utilized to calculate the initial influence of topics, papers, authors, and journals in a homogeneous network, whereas the HITS algorithm is utilized to express the mutual reinforcement between topics, papers, authors, and journals in a heterogeneous network, iteratively calculating the final topic influence value. Based on a specific interdisciplinary domain, social media data, we applied the 4ERRank model to the 19,527 topics included in the criteria. The experimental results demonstrate that the 4ER-Rank model can successfully synthesize the performance of classic co-word metrics and effectively reflect high citation topics. This study enriches the methodology for assessing topic impact and contributes to the development of future topic-based retrieval and prediction tasks.
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