Clustering method for predicting campaign results based on voter and candidate characteristics
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Published:
Aug 23, 2023
Keywords:
Clustering methods, Campaign strategy, K-Means, Political election, Voter preferences
Section
Computer Science
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Abstract
This research applies clustering method with K-Means algorithm to analyze voter preferences and predict campaign outcomes based on voter and candidate characteristics in the context of political elections. By collecting and processing data on age, education, occupation, and candidate preferences, we apply K-Means to cluster voters into groups with similar patterns. The cluster results reveal similar political views and candidate preferences within each group of voters. By correlating the cluster results with previous election data, we are able to predict campaign outcomes with an accuracy that is beneficial for more careful and effective campaign strategies. This research contributes to a deeper understanding of the use of clustering methods in the context of political elections and its relevance in formulating successful campaign strategies.
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How to Cite
Manurung, J., Ramen, S. . and Logaraj, L. (2023) “Clustering method for predicting campaign results based on voter and candidate characteristics”, Jurnal Mantik, 7(2), pp. 1402-1408. doi: 10.35335/mantik.v7i2.4108.
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Bossetta, M., & Schmøkel, R. (2023). Cross-platform emotions and audience engagement in social media political campaigning: Comparing candidates’ Facebook and Instagram images in the 2020 US election. Political Communication, 40(1), 48–68.
Brito, K. D. S., Silva Filho, R. L. C., & Adeodato, P. J. L. (2021). A systematic review of predicting elections based on social media data: research challenges and future directions. IEEE Transactions on Computational Social Systems, 8(4), 819–843.
Bruter, M., & Harrison, S. (2020). Inside the mind of a voter: A new approach to electoral psychology. Princeton University Press.
dos Santos Brito, K., & Adeodato, P. J. L. (2020). Predicting Brazilian and US elections with machine learning and social media data. 2020 International Joint Conference on Neural Networks (IJCNN), 1–8.
Enríquez, J. G., Jiménez-Ramírez, A., Domínguez-Mayo, F. J., & García-García, J. A. (2020). Robotic process automation: a scientific and industrial systematic mapping study. IEEE Access, 8, 39113–39129.
Fernandes, J. M., Debus, M., & Bäck, H. (2021). Unpacking the politics of legislative debates. European Journal of Political Research, 60(4), 1032–1045.
Ha, S., & Jeong, H. (2021). Unraveling hidden interactions in complex systems with deep learning. Scientific Reports, 11(1), 12804.
Hegazy, I. M. (2021). The effect of political neuromarketing 2.0 on election outcomes: The case of Trump’s presidential campaign 2016. Review of Economics and Political Science, 6(3), 235–251.
Hendriks, C. M., Ercan, S. A., & Boswell, J. (2020). Mending democracy: democratic repair in disconnected times. Oxford University Press, USA.
Howard, P. N. (2020). Lie machines: How to save democracy from troll armies, deceitful robots, junk news operations, and political operatives. Yale University Press.
Howard, P. N., & Hussain, M. M. (2013). Democracy’s fourth wave?: digital media and the Arab Spring. Oxford University Press.
Huang, J., He, D., Obaidat, M. S., Vijayakumar, P., Luo, M., & Choo, K.-K. R. (2021). The application of the blockchain technology in voting systems: A review. ACM Computing Surveys (CSUR), 54(3), 1–28.
Johnson, A. R. (2022). Anthropogenic Influences on the Decline, Restoration, and Eco-Evolutionary Dynamics of Lake Superior’s Coaster Brook Trout. Michigan Technological University.
Lavezzolo, S., Ramiro, L., & FERNÁNDEZ?VÁZQUEZ, P. (2022). Technocratic attitudes in COVID?19 times: Change and preference over types of experts. European Journal of Political Research, 61(4), 1123–1142.
Li, L., & Zhang, J. (2021). Research and analysis of an enterprise E-commerce marketing system under the big data environment. Journal of Organizational and End User Computing (JOEUC), 33(6), 1–19.
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 121390.
Miller, J. D., Woods, L. T., & Kalmbach, J. (2022). The impact of the Covid-19 pandemic in a polarized political system: Lessons from the 2020 election. Electoral Studies, 80, 102548.
Neshenko, N., Nader, C., Bou-Harb, E., & Furht, B. (2020). A survey of methods supporting cyber situational awareness in the context of smart cities. Journal of Big Data, 7(1), 1–41.
Nie, X., Qin, D., Zhou, X., Duo, H., Hao, Y., Li, B., & Liang, G. (2023). Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges. Computers in Biology and Medicine, 106939.
Rodrigues, A. A., Brito, D. R. B., Kono, I. S., Reis, S. S., & ... (2022). Seroprevalence of Neospora caninum and risk factors associated with infection in water buffaloes (Bubalus bubalis) from Maranhão State, Brazil. …?: Regional Studies and …. https://www.sciencedirect.com/science/article/pii/S2405939021001337
Schuster, S. S. (2020). Does campaign spending affect election outcomes? New evidence from transaction-level disbursement data. The Journal of Politics, 82(4), 1502–1515.
Wang, Y., Sun, Y., Huang, Y., Liu, Z., Gao, S., Zhang, W., Ge, W., & Zhang, W. (2022). Ferv39k: A large-scale multi-scene dataset for facial expression recognition in videos. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 20922–20931.
Xavier, D. R., e Silva, E. L., Lara, F. A., e Silva, G. R. R., Oliveira, M. F., Gurgel, H., & Barcellos, C. (2022). Involvement of political and socio-economic factors in the spatial and temporal dynamics of COVID-19 outcomes in Brazil: A population-based study. The Lancet Regional Health–Americas, 10.
Zhukov, D., Khvatova, T., Millar, C., & Andrianova, E. (2022). Beyond big data–new techniques for forecasting elections using stochastic models with self-organisation and memory. Technological Forecasting and Social Change, 175, 121425.
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