Evaluation of Manuscript Preference of Sci-Tech Periodicals: Structural Topic Models of Computer Engineering Related Publications in China

Volume 7, Issue 2, April 2022     |     PP. 22-43      |     PDF (1770 K)    |     Pub. Date: April 6, 2022
DOI: 10.54647/dee47268    88 Downloads     244173 Views  

Author(s)

Li Xiang, McGill University, Canada

Abstract
To help scholars in computer engineering get to know the state-of-the-art topics and choose suitable journals to publish their work, the method that uses structural topic model (STM) to extract the topics of papers and uses the generalized linear model to analyze the influence of time on topic prevalence. Based on these results, the manuscript preferences of a journal are extracted. As an empirical experiment, a total number of 10320 papers are extracted from 3 different Chinese core computer engineering journals to analyze. As a result, 6 hotspots in the past 6 years in the Chinese computer engineering field are extracted, and the changes in topic prevalence in the Chinese computer engineering field over time are also studied. We believe our method could help researchers learn the most cutting-edge topics and choose suitable journals for them.

Keywords
Information processing, computer engineering, academic papers publishing, manuscript preference, structural topic model

Cite this paper
Li Xiang, Evaluation of Manuscript Preference of Sci-Tech Periodicals: Structural Topic Models of Computer Engineering Related Publications in China , SCIREA Journal of Electrical Engineering. Volume 7, Issue 2, April 2022 | PP. 22-43. 10.54647/dee47268

References

[ 1 ] Alexander, E., & Gleicher, M. (2015). Task-driven comparison of topic models. IEEE transactions on visualization and computer graphics, 22(1), 320-329.
[ 2 ] Asuncion, A., Welling, M., Smyth, P., & Teh, Y. W. (2012). On smoothing and inference for topic models. arXiv preprint arXiv:1205.2662.
[ 3 ] Bischof, J., & Airoldi, E. M. (2012). Summarizing topical content with word frequency and exclusivity. In Proceedings of the 29th International Conference on Machine Learning (ICML-12) (pp. 201-208).
[ 4 ] Blei, D., Carin, L., & Dunson, D. (2010). Probabilistic topic models. IEEE signal processing magazine, 27(6), 55-65.
[ 5 ] Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of science. The annals of applied statistics, 1(1), 17-35.
[ 6 ] Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3, 993-1022.
[ 7 ] Chandelier, M., Steuckardt, A., Mathevet, R., Diwersy, S., & Gimenez, O. (2018). Content analysis of newspaper coverage of wolf recolonization in France using structural topic modeling. Biological conservation, 220, 254-261.
[ 8 ] Chen, X., Zou, D., Cheng, G., & Xie, H. (2020). Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education, 151, 103855.
[ 9 ] Dai J. (2014, 10). The inner logic of core academic journals accepting manuscript within university field——Based on the statistic of the publication of 14 higher education type Chinese core journals (2006~2012). Heilongjiang Researches on Higher Education (pp. 29-33). doi: CNKI: SUN: HLJG.0.2014-10-010.
[ 10 ] Hu, N., Zhang, T., Gao, B., & Bose, I. (2019). What do hotel customers complain about? Text analysis using structural topic model. Tourism Management, 72, 417-426.
[ 11 ] Lee, J. M., Kwon, O. J., Lee, H. S., Coh, B. Y., & Park, Y. W. (2011, November). A research on the method to select promising scientific technologies in the condensed matter physics by using journal's editing preference. In Proceedings of the 2011 ACM Symposium on Research in Applied Computation (pp. 216-219).
[ 12 ] Lei Y. (2021, 32). Association between impacts of English scientific journals and reviewers' academic and reviewing performance: A Publons-based empirical study of medical journals. Chinese Journal of Scientific and Technical Periodicals (pp. 206-213). doi: 10.11946/cjstp.202006170598
[ 13 ] Li Y., Wen L., Song X., & Guan X.. (2021, 04). Research Status, Theoretical Hotspots and Forward Trends of Tea Science——Based on CiteSpace Visual Analysis of Articles Published in Six Journals Over the Past 20 Years. Journal of Tea Communication (736-743).
[ 14 ] Qiu, L., & Yu, J. (2018). CLDA: An effective topic model for mining user interest preference under big data background. Complexity, 2018.
[ 15 ] Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). Stm: An R package for structural topic models. Journal of Statistical Software, 91(1), 1-40.
[ 16 ] Roberts, M. E., Stewart, B. M., Tingley, D., & Airoldi, E. M. (2013, December). The structural topic model and applied social science. In Advances in neural information processing systems workshop on topic models: computation, application, and evaluation (Vol. 4, pp. 1-20).
[ 17 ] Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder‐Luis, J., Gadarian, S. K., ... & Rand, D. G. (2014). Structural topic models for open‐ended survey responses. American Journal of Political Science, 58(4), 1064-1082.
[ 18 ] Séaghdha, D. O. (2010, July). Latent variable models of selectional preference. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (pp. 435-444).
[ 19 ] Shi, L., Song, G., Cheng, G., & Liu, X. (2020). A user-based aggregation topic model for understanding user’s preference and intention in social network. Neurocomputing, 413, 1-13.
[ 20 ] Sriurai, W., Meesad, P., & Haruechaiyasak, C. (2009). Recommending related articles in wikipedia via a topic-based model. In 9th International Conference On Innovative Internet Community Systems I2CS 2024. Gesellschaft für Informatik eV.
[ 21 ] Taddy, M. (2012, March). On estimation and selection for topic models. In Artificial Intelligence and Statistics (pp. 1184-1193). PMLR.
[ 22 ] Xu, G., Zhang, Y., & Yi, X. (2008, December). Modelling user behaviour for web recommendation using lda model. In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (Vol. 3, pp. 529-532). IEEE.
[ 23 ] Zhao, F., Zhu, Y., Jin, H., & Yang, L. T. (2016). A personalized hashtag recommendation approach using LDA-based topic model in microblog environment. Future Generation Computer Systems, 65, 196-206.
[ 24 ] Zhou X., Chen D., & Liang W.. (2020, 39). Does the Editorial Bias Exist in Elite Chinese Economics Journals? South China Journal of Economics (pp. 105-124).