Research on the hysteresis effect of topic related evolution for emerging trends prediction
By:
References
- Aranda-Michel, E., Luketich, J. D., Rao, R., Morell, V. O., Arnaoutakis, G. J., Kilic, A., Dunn-Lewis, C., & Sultan, I. (2022). The effect of receiving an award from the American Association for Thoracic Surgery Foundation. JTCVS Open, 10, 282–289. https://doi.org/10.1016/j.xjon.2021.10.066.
- Azzopardi, L., Girolami, M., & Van Risjbergen, K. (2003). Investigating the relationship between language model perplexity and IR precision-recall measures. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 369-370. https://doi.org/10.1145/860435.860505.
- Baerwald, T. J., Firth, P., & Ruth, S. L. (2016). The Dynamics of Coupled Natural and Human Systems Program at the U.S. National Science Foundation: lessons learned in interdisciplinary funding program development and management. Current Opinion in Environmental Sustainability, 19, 123–133. https://doi.org/10.1016/j.cosust.2016.02.001.
- Bailey, K. D. (2006). Systems theory. In Springer eBooks (pp. 379–401). https://doi.org/10.1007/0-387-36274-6_19.
- Bermudez-Edo, M., Barnaghi, P., & Moessner, K. (2018). Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation. Automation in Construction, 88, 87–100. https://doi.org/10.1016/j.autcon.2017.12.036.
- Blei D. M., Ng A. Y., & Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022. https://doi.org/10.1162/jmlr.2003.3.4-5.993.
- Bozeman, B., & Youtie, J. (2017). Socio-economic impacts and public value of government-funded research: Lessons from four US National Science Foundation initiatives. Research Policy, 46(8), 1387–1398. https://doi.org/10.1016/j.respol.2017.06.003.
- Ebadi, A., Auger, A., & Gauthier, Y. (2022). Detecting emerging technologies and their evolution using deep learning and weak signal analysis. Journal of Informetrics, 16(4), 101344. https://doi.org/10.1016/j.joi.2022.101344.
- Fruchterman, T.M.J. and Reingold, E.M. (1991), Graph drawing by force-directed placement. Softw: Pract. Exper., 21: 1129-1164. https://doi.org/10.1002/spe.4380211102.
- Funkner, A. A., Yakovlev, A. N., & Kovalchuk, S. V. (2022). Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways. Journal of Computational Science, 59, 101562. https://doi.org/10.1016/j.jocs.2022.101562.
- Giannopoulos, G., & Munro, J. (2019). Innovation Ecosystems—A Systems-Based Theory of Innovation. In Elsevier eBooks (pp. 19–41). https://doi.org/10.1016/b978-0-12-813804-5.00002-4.
- Gibson, E., Daim, T. U., & Dabić, M. (2019). Evaluating university industry collaborative research centers. Technological Forecasting and Social Change, 146, 181–202. https://doi.org/10.1016/j.techfore.2019.05.014.
- Gozuacik, N., Sakar, C. O., & Ozcan, S. (2023). Technological forecasting based on estimation of word embedding matrix using LSTM networks. Technological Forecasting and Social Change, 191, 122520. https://doi.org/10.1016/j.techfore.2023.122520
- Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing and Management, 54(6), 1292–1307. https://doi.org/10.1016/j.ipm.2018.05.006.
- Hauptmann, E. (2022). Book review. Studies in History and Philosophy of Science, 94, 206–207. https://doi.org/10.1016/j.shpsa.2022.02.009
- Hu, K., Luo, Q., Qi, K., Yang, S., Mao, J., Fu, X., Zheng, J., Wu, H., Guo, Y., & Zhu, Q. (2019). Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis. Information Processing and Management, 56(4), 1185–1203. https://doi.org/10.1016/j.ipm.2019.02.014
- Huang, L., Chen, X., Ni, X., Liu, J., Cao, X., & Wang, C. (2021). Tracking the dynamics of co-word networks for emerging topic identification. Technological Forecasting and Social Change, 170, 120944. https://doi.org/10.1016/j.techfore.2021.120944
- Liang, Z., Mao, J., Lu, K., Ba, Z., & Li, G. (2021). Combining deep neural network and bibliometric indicator for emerging research topic prediction. Information Processing and Management, 58(5), 102611. https://doi.org/10.1016/j.ipm.2021.102611
- Liu, Z., Xu, H., Yue, L. (2018). Research on Lagging Effect of Topic Diffusion Evolution Face to Prediction of Research Front. Journal of the China Society for Scientific and Technical Information, 37(10), 979-988.
- Lu, K., Yang, G., & Wang, X. (2022). Topics emerged in the biomedical field and their characteristics. Technological Forecasting and Social Change, 174, 121218. https://doi.org/10.1016/j.techfore.2021.121218
- Mejia, C., & Kajikawa, Y. (2020). Emerging topics in energy storage based on a large-scale analysis of academic articles and patents. Applied Energy, 263, 114625. https://doi.org/10.1016/j.apenergy.2020.114625.
- Moehrle, M. G. (2019). Similarity measurement in times of topic modelling. World Patent Information, 59, 101934. https://doi.org/10.1016/j.wpi.2019.101934
- Newman, D., & Lau, J., & Grieser, K., & Baldwin, T. (2010). Automatic Evaluation of Topic Coherence.. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. In Association for Computational Linguistics, 100-108.
- Nichols, L. (2014). A topic model approach to measuring interdisciplinarity at the National Science Foundation. Scientometrics, 100(3), 741–754. https://doi.org/10.1007/s11192-014-1319-2
- Popper, K. R. (1972). Objective knowledge: An Evolutionary Approach. Oxford: Clarendon Press.
- Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237–255. https://doi.org/10.1016/0040-1625(95)00022-3
- Prabhaa, S. S., Bindu, N., Manoj, P., & Kumar, K. S. (2020). Citation network analysis of plastic electronics: Tracing the evolution and emerging research fronts. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.04.236
- Salleh, F. H. M., Arif, S. M., Zainudin, S., & Firdaus-Raih, M. (2015). Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient. Computational Biology and Chemistry, 59, 3–14. https://doi.org/10.1016/j.compbiolchem.2015.04.012.
- Savin, I. (2023). Evolution and recombination of topics in Technological Forecasting and Social Change. Technological Forecasting and Social Change, 194, 122723. https://doi.org/10.1016/j.techfore.2023.122723
- Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274–282. https://doi.org/10.1016/j.techfore.2010.07.006
- Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450–1467. https://doi.org/10.1016/j.respol.2014.02.005
- Tamakloe, R., & Park, D. (2023). Discovering latent topics and trends in autonomous vehicle-related research: A structural topic modelling approach. Transport Policy, 139, 1–20. https://doi.org/10.1016/j.tranpol.2023.06.001
- Wang, X., He, J., Huang, H., & Wang, H. (2022). MatrixSim: A new method for detecting the evolution paths of research topics. Journal of Informetrics, 16(4), 101343. https://doi.org/10.1016/j.joi.2022.101343
- Xu, H., Winnink, J., Yue, Z., Zhang, H., & Pang, H. (2021). Multidimensional Scientometric indicators for the detection of emerging research topics. Technological Forecasting and Social Change, 163, 120490. https://doi.org/10.1016/j.techfore.2020.120490
- Xu, S., Hao, L., Yang, G., Lu, K., & An, X. (2021). A topic models based framework for detecting and forecasting emerging technologies. Technological Forecasting and Social Change, 162, 120366. https://doi.org/10.1016/j.techfore.2020.120366
- Yang, J., Lu, W., Hu, J., & Huang, S. (2022). A novel emerging topic detection method: A knowledge ecology perspective. Information Processing and Management, 59(2), 102843. https://doi.org/10.1016/j.ipm.2021.102843
- Ye, G., Wang, C., Wu, C., Peng, Z., Wei, J., Song, X., Tan, Q., & Wu, L. (2023). Research frontier detection and analysis based on research grants information: A case study on health informatics in the US. Journal of Informetrics, 17(3), 101421. https://doi.org/10.1016/j.joi.2023.101421
- Zhang, Y., Guo, Y., Wang, X., Zhu, D., & Porter, A. L. (2013). A hybrid visualisation model for technology roadmapping: bibliometrics, qualitative methodology and empirical study. Technology Analysis & Strategic Management, 25(6), 707–724. https://doi.org/10.1080/09537325.2013.803064
- Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu, D., & Lu, J. (2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological Forecasting and Social Change, 105, 179–191. https://doi.org/10.1016/j.techfore.2016.01.015
Language: English
Page range: 52 - 77
Submitted on: Oct 30, 2024
Accepted on: Mar 13, 2025
Published on: Apr 4, 2025
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2025 Ziqiang Liu, Haiyun Xu, Lixin Yue, Zenghui Yue, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution 4.0 License.