Predicting Popularity of Emerging Topics with Multivariable LSTM and Bibliometric Indicators
Chen Wen1,2,Chen Wei1,2,3()
1Wuhan Documentation and Information Center, Chinese Academy of Sciences, Wuhan 430071, China 2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China 3Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071, China
[Objective] This paper identifies emerging topics from multi-source data, and constructs a multivariable LSTM with bibliometric indicators to predict their popularity. [Methods] Firstly, we explored the topics of funded projects, papers and patents. Secondly, we identified the emerging ones based on their novelty, growth and persistence. Finally, we predicted these topics’ popularity with the multivariable LSTM model and indicators of funding amounts, number of fundings, average citation counts for each article, and number of patent IPC subclasses. [Results] We examined our new model with studies on solid oxide fuel cell, which yielded better performance than BP, KNN, SVM and univariate LSTM. Our model had the lowest MAE (16.534) and RMSE (23.494), as well as the highest R2 (0.642). [Limitations] We did not include each patent’s citation number because it was difficult to obtain specific data for each time window. [Conclusions] The modified LSMT could effectively predict the popularity of emerging topics.
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