Bayesian networks for inferring the relationship between individual behavior and social influence: A case study of early 20th century British travels in China
Vol 9, Issue 7, 2024, Article identifier:
VIEWS - 392 (Abstract) 99 (PDF)
Abstract
This study investigated the possibility of applying the Bayesian networks (BNs) in analyzing the relationship between individual behavior and social influence among early 20th-century British travelers in China. While historical studies have provided valuable details about social interactions, existing research using such studies has shown limitations in quantifying and analyzing complex relationships. This study attempts to address this gap by employing Bayesian networks (BNs) to construct a framework for modeling the probabilistic relationships between various factors influencing the travel patterns of British travelers in China in the early 20th century. These factors include political climate, economic considerations, and cultural interactions, which are sourced through historical studies, travel diaries, and other contemporary sources. The performance of the proposed Bayesian network model is evaluated using established statistical methods, including confusion matrices, cross-validation, and sensitivity analysis (SA). The results have shown the significance of the chosen model in analyzing the complex relationship selected analysis.
Keywords
Full Text:
PDFReferences
1. Veracini, L. (2022). Colonialism: a global history. Routledge.
2. Siu, H. F. (2016). Tracing China: A forty-year ethnographic journey. Hong Kong University Press.
3. Eichstaedt, J. C., Kern, M. L., Yaden, D. B., Schwartz, H. A., Giorgi, S., Park, G., ... & Ungar, L. H. (2021). Closed-and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations. Psychological Methods, 26(4), 398.
4. Quintana, R. (2023). Embracing complexity in social science research. Quality & Quantity, 57(1), 15-38.
5. Mehmetoglu, M., & Jakobsen, T. G. (2022). Applied statistics using Stata: a guide for the social sciences. Sage.
6. Rounsevell, M. D., Arneth, A., Brown, C., Cheung, W. W., Gimenez, O., Holman, I., ... & Shin, Y. J. (2021). Identifying uncertainties in scenarios and models of socio-ecological systems in support of decision-making. One Earth, 4(7), 967-985.
7. J. Zheng, S. He, and C. Lv, Application of Bayesian Network in Effectiveness Evaluation of High-Level Construction of Specialized Group, 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ), Beijing, China, 2022, pp. 597-601, doi: 10.1109/IAEAC54830.2022.9929943.
8. S. T. Low, M. S. Mohamad, S. Omatu, L. E. Chai, S. Deris and M. Yoshioka, Inferring gene regulatory networks from perturbed gene expression data using a dynamic Bayesian network with a Markov Chain Monte Carlo algorithm, 2014 IEEE International Conference on Granular Computing (GrC), Noboribetsu, Japan, 2014, pp. 179-184, doi: 10.1109/GRC.2014.6982831.
9. Y. Miyakoshi and S. Kato, Facial emotion detection considering partial occlusion of face using Bayesian network, 2011 IEEE Symposium on Computers & Informatics, Kuala Lumpur, Malaysia, 2011, pp. 96-101, doi: 10.1109/ISCI.2011.5958891.
10. M. Nouh and J. R. C. Nurse, Identifying Key Players in Online Activist Groups on the Facebook Social Network, 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA, 2015, pp. 969-978, doi: 10.1109/ICDMW.2015.88.
11. A. Alsayat and H. El-Sayed, Social media analysis using optimized K-Means clustering, 2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA), Towson, MD, USA, 2016, pp. 61-66, doi: 10.1109/SERA.2016.7516129.
12. E. Otte and R. Rousseau, Social network analysis: a powerful strategy also for the information sciences, Journal of Information Science, vol. 28, no. 6, pp. 441-453, 2002.
13. T. C. Haas and S. M. Ferreira, Federated databases and actionable intelligence: using social network analysis to disrupt transnational wildlife trafficking criminal networks, Security Informatics, vol. 4, no. 2, 2015.
14. T. Opsahl, F. Agneessens and J. Skvoretz, Node centrality in weighted networks: Generalizing degree and shortest paths, Social Networks, vol. 32, no. 3, pp. 245-251, 2010.
15. David Lazer, Alex Sandy Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, et al., Life in the network: the coming age of computational social science in Science, New York, NY, vol. 323, no. 5915, pp. 721, 2009.
16. Kevin Lerman, Ari Gilder, Mark Dredze and Fernando Pereira, Reading the markets: Forecasting public opinion of political candidates by news analysis, Proceedings of the 22nd International Conference on Computational Linguistics-Volume, vol. 1, pp. 473-480, 2008.
17. Y. Fan and Q. Peng, Inferring gene regulatory networks based on spline regression and Bayesian group lasso, 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai, China, 2016, pp. 39-42, doi: 10.1109/SNPD.2016.7515875.
18. C. Wang, X. Gao and X. Li, An Interpretable Deep Bayesian Model for Facial Micro-Expression Recognition, 2023 8th International Conference on Control and Robotics Engineering (ICCRE), Niigata, Japan, 2023, pp. 91-94, doi: 10.1109/ICCRE57112.2023.10155596.
19.
20. J. Zheng, Z. Wei and W. Zheng, The casual inference of road traffic accidents based on the Bayesian network optimization, 2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI), Prague, Czech Republic, 2023, pp. 185-193, doi: 10.1109/ACEDPI58926.2023.00044.
21. Q. Wang et al., Fault Intelligent diagnosis of reversible pumped storage Unit based on Bayesian networks and counterfactual reasoning, 2023 8th Asia Conference on Power and Electrical Engineering (ACPEE), Tianjin, China, 2023, pp. 2018-2022, doi: 10.1109/ACPEE56931.2023.10135793.
22. N. Sharmin, S. Roy, A. Laszka, J. Acosta and C. Kiekintveld, Bayesian Models for Node-Based Inference Techniques, 2023 IEEE International Systems Conference (SysCon), Vancouver, BC, Canada, 2023, pp. 1-8, doi: 10.1109/SysCon53073.2023.10131168.
DOI: https://doi.org/10.59429/esp.v9i7.2077
(392 Abstract Views, 99 PDF Downloads)
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Meijie Ding
This work is licensed under a Creative Commons Attribution 4.0 International License.