Use of Big Data in Political Communication Analysis: Forming a Targeting and Electorate Segmentation Strategy
Abstract
In this digital era, political communication has become increasingly complex with the emergence of various social media platforms and information technology. This allows for greater interaction between political leaders, parties and the electorate. In this context, the use of Big Data has become crucial in collecting, analyzing and understanding the political behavior patterns of the electorate. This research aims to explore the use of Big Data in the context of political communication analysis, especially in forming targeting and electorate segmentation strategies. Through this approach, it is hoped that an effective method can be found in understanding mass political preferences. This research uses a qualitative and quantitative approach by analyzing Big Data data from various social media platforms, online surveys and other digital data sources. Statistical analysis and machine learning techniques are also used to identify patterns of electoral behavior. The research results show that the use of Big Data in political communication analysis provides deep insight into the preferences and needs of the electorate. By utilizing available data, targeting and electorate segmentation strategies can be prepared more precisely and effectively. The conclusion of this research is the analysis of political communication, the use of Big Data has proven its value in forming targeting and electorate segmentation strategies. With an integrated approach between qualitative and quantitative data, political leaders and parties can better understand political dynamics and increase the effectiveness of communication with the electorate.
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References
Aagaard, P. (2019). Big data in political communication. In J. S. Pedersen & A. Wilkinson (Eds.), Big Data. Edward Elgar Publishing. https://doi.org/10.4337/9781788112352.00020
Agarwal, P., Sharma, S., & Matta, P. (2022). Big Data Technologies in UAV’s Traffic Management System: Importance, Benefits, Challenges and Applications. In R. Rawat, A. M. Sowjanya, S. I. Patel, V. Jaiswal, I. Khan, & A. Balaram (Eds.), Autonomous Vehicles Volume 1 (1st ed., pp. 181–201). Wiley. https://doi.org/10.1002/9781119871989.ch10
Alam, A. (2023). Cloud-Based E-learning: Scaffolding the Environment for Adaptive E-learning Ecosystem Based on Cloud Computing Infrastructure. In S. C. Satapathy, J. C.-W. Lin, L. K. Wee, V. Bhateja, & T. M. Rajesh (Eds.), Computer Communication, Networking and IoT (Vol. 459, pp. 1–9). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-1976-3_1
Anjana, & Mukerji, S. (2023). Strategic Planning and Policy Framework for Implementation of Blockchain Technology in Education in India: In G. Kurubacak, R. C. Sharma, & H. Y?ld?r?m (Eds.), Advances in Electronic Government, Digital Divide, and Regional Development (pp. 51–68). IGI Global. https://doi.org/10.4018/978-1-6684-4153-4.ch003
Arzt, M., Deschamps, J., Schmied, C., Pietzsch, T., Schmidt, D., Tomancak, P., Haase, R., & Jug, F. (2022). LABKIT: Labeling and Segmentation Toolkit for Big Image Data. Frontiers in Computer Science, 4, 777728. https://doi.org/10.3389/fcomp.2022.777728
Balakrishnan, R., Valdés Hernández, M. D. C., & Farrall, A. J. (2021). Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data – A systematic review. Computerized Medical Imaging and Graphics, 88, 101867. https://doi.org/10.1016/j.compmedimag.2021.101867
Baraldi, A., Sapia, L. D., Tiede, D., Sudmanns, M., Augustin, H. L., & Lang, S. (2023). Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 1: Problem background in Artificial General Intelligence (AGI). Big Earth Data, 7(3), 455–693. https://doi.org/10.1080/20964471.2021.2017549
Cai, G. (2021). Accurate mining of location data in the communication field based on big data. Journal of High Speed Networks, 27(3), 251–264. https://doi.org/10.3233/JHS-210665
Cho, E.-J., Jeong, T.-D., Kim, S., Park, H.-D., Yun, Y.-M., Chun, S., & Min, W.-K. (2023). A New Strategy for Evaluating the Quality of Laboratory Results for Big Data Research: Using External Quality Assessment Survey Data (2010–2020). Annals of Laboratory Medicine, 43(5), 425–433. https://doi.org/10.3343/alm.2023.43.5.425
Dolata, U., & Schrape, J.-F. (2022). Internet, Big Data und digitale Plattformen: Politische Ökonomie – Kommunikation – Regulierung: Eine kurze Einführung in das Sonderheft. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 74(S1), 1–9. https://doi.org/10.1007/s11577-022-00843-6
Ehsani, F., & Hosseini, M. (2023). Consumer Segmentation Based on Location and Timing Dimensions Using Big Data from Business-to-Customer Retailing Marketplaces. Big Data, big.2022.0307. https://doi.org/10.1089/big.2022.0307
Fu, Y., Sun, X., & Wang, W. (2023). The Optimization of Global Organizational Communication for Enterprise Supply Organization Management by Using Big Data Text Mining: Journal of Global Information Management, 31(3), 1–17. https://doi.org/10.4018/JGIM.324608
Guo, Y., Chen, Y., Xie, Y., & Ban, X. (2022). An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education. Information, 13(4), 197. https://doi.org/10.3390/info13040197
Hou, L., Ma, C., & Yang, L. (2020). A novel encryption algorithm for unstructured big data in wireless communication network. International Journal of Internet Protocol Technology, 13(3), 124. https://doi.org/10.1504/IJIPT.2020.107970
Karina, D., Rishi, P., & Rashmi, G. (2023). Social Media Utilization for Student Learning Success Effectiveness. World Psychology, 2(1), 54–64. https://doi.org/10.55849/wp.v2i1.392
Karthikeyan, Khang, A., & Krishnaveni, K. (2023). Big Data Opportunities: Lung Image Segmentation for a Coronary Artery Diseases Monitoring System. In A. Khang (Ed.), Advances in Medical Technologies and Clinical Practice (pp. 314–323). IGI Global. https://doi.org/10.4018/979-8-3693-0876-9.ch019
Khalemsky, A., & Gelbard, R. (2020). A dynamic classification unit for online segmentation of big data via small data buffers. Decision Support Systems, 128, 113157. https://doi.org/10.1016/j.dss.2019.113157
Khalemsky, A., & Gelbard, R. (2021). ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time. Journal of Data and Information Quality, 13(2), 1–27. https://doi.org/10.1145/3434778
Kuki, N., Walmsley, D. L., Kanai, K., Takechi, S., Yoshida, M., Murakami, R., Takano, K., Tominaga, Y., Takahashi, M., Ito, S., Nakao, N., Angove, H., Baker, L. M., Carter, E., Dokurno, P., Le Strat, L., Macias, A. T., Molyneaux, C.-A., Murray, J. B., … Hubbard, R. E. (2023). A covalent fragment-based strategy targeting a novel cysteine to inhibit activity of mutant EGFR kinase. RSC Medicinal Chemistry, 14(12), 2731–2737. https://doi.org/10.1039/D3MD00439B
Lang, L., Zhou, S., Zhong, M., Sun, G., Pan, B., & Guo, P. (2023). A Big Data Based Dynamic Weight Approach for RFM Segmentation. Computers, Materials & Continua, 74(2), 3503–3513. https://doi.org/10.32604/cmc.2023.023596
Liana Nurhaeti. (2023). Information And Communication Technology- Based Learning Models In Islamic Religious Education. DIROSAT: Journal of Education, Social Sciences & Humanities, 1(1), 1–6. https://doi.org/10.58355/dirosat.v1i1.1
Löffler, N. (2023). Trusting tech firms’ big data for political microtargeting? A qualitative analysis of parties’ communication managers risk and trust perceptions. Journal of Information Technology & Politics, 1–15. https://doi.org/10.1080/19331681.2023.2264299
Ma, F., Gao, F., Sun, J., Zhou, H., & Hussain, A. (2019). Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data. Remote Sensing, 11(21), 2586. https://doi.org/10.3390/rs11212586
Macgilchrist, F. (2019). Cruel optimism in edtech: When the digital data practices of educational technology providers inadvertently hinder educational equity. Learning, Media and Technology, 44(1), 77–86. https://doi.org/10.1080/17439884.2018.1556217
Mavriki, P., & Karyda, M. (2019). Big data in political communication: Implications for group privacy. International Journal of Electronic Governance, 11(3/4), 289. https://doi.org/10.1504/IJEG.2019.103716
Mudigonda, P., & Abburi, S. K. (2020). A Survey: 5G in IoT is a Boon for Big Data Communication and Its Security. In A. Kumar, M. Paprzycki, & V. K. Gunjan (Eds.), ICDSMLA 2019 (Vol. 601, pp. 318–327). Springer Singapore. https://doi.org/10.1007/978-981-15-1420-3_33
Park, S.-Y., & Loo, B. T. (2022). The Use of Crowdfunding and Social Media Platforms in Strategic Start-up Communication: A Big-data Analysis. International Journal of Strategic Communication, 16(2), 313–331. https://doi.org/10.1080/1553118X.2022.2032079
Pham, T. C., Nguyen, V.-N., Choi, Y., Lee, S., & Yoon, J. (2021). Recent Strategies to Develop Innovative Photosensitizers for Enhanced Photodynamic Therapy. Chemical Reviews, 121(21), 13454–13619. https://doi.org/10.1021/acs.chemrev.1c00381
Satish Kumar, A., & Revathy, S. (2022). A hybrid soft computing with big data analytics based protection and recovery strategy for security enhancement in large scale real world online social networks. Theoretical Computer Science, 927, 15–30. https://doi.org/10.1016/j.tcs.2022.05.018
Susanti, L. E., & Ekasani, K. A. (2021). Facebook Automatic Translation: Ketidakkonsistensian Penyampaian Arti Bahasa bagi Penggunanya. Jurnal Kajian Bahasa, Sastra Dan Pengajaran (KIBASP), 5(1), 107–120. https://doi.org/10.31539/kibasp.v5i1.2694
Taylor, C. R. (2019). Editorial: Artificial intelligence, customized communications, privacy, and the General Data Protection Regulation (GDPR). International Journal of Advertising, 38(5), 649–650. https://doi.org/10.1080/02650487.2019.1618032
Ullah, F., Salam, A., Abrar, M., & Amin, F. (2023). Brain Tumor Segmentation Using a Patch-Based Convolutional Neural Network: A Big Data Analysis Approach. Mathematics, 11(7), 1635. https://doi.org/10.3390/math11071635
Wang, B. (2022). Wireless Multifunctional Display Platform for Visual Communication Design Based on IoT Big Data. Mobile Information Systems, 2022, 1–12. https://doi.org/10.1155/2022/9270271
Wang, X., & Yang, J. (2023). A Big Data-Driven Deep Transfer Learning Approach for Path Loss Prediction in Mobile Communications. Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence, 584–588. https://doi.org/10.1145/3594315.3594375
Yang, B., Xiong, X., Liu, H., Jia, Y., Gao, Y., Tolba, A., & Zhang, X. (2023). Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis. Sensors, 23(15), 6795. https://doi.org/10.3390/s23156795
Yao, Y., Feng, C., Xie, J., Yan, X., Guan, Q., Han, J., Zhang, J., Ren, S., Liang, Y., & Luo, P. (2023). A site selection framework for urban power substation at micro?scale using spatial optimization strategy and geospatial big data. Transactions in GIS, 27(6), 1662–1679. https://doi.org/10.1111/tgis.13093
Zhang, D., & Huang, M. (2022). A Precision Marketing Strategy of e-Commerce Platform Based on Consumer Behavior Analysis in the Era of Big Data. Mathematical Problems in Engineering, 2022, 1–8. https://doi.org/10.1155/2022/8580561
Zhao, Y., & Ouyang, W. (2022). Wireless Communication Network Security System Based on Big Data Information Transmission Technology. Wireless Communications and Mobile Computing, 2022, 1–6. https://doi.org/10.1155/2022/1066331
Zhou, R. (2022). A Heuristic Task Scheduling Strategy for Intelligent Manufacturing in the Big Data-Driven Fog Computing Environment. Mobile Information Systems, 2022, 1–10. https://doi.org/10.1155/2022/5830760
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Copyright (c) 2024 Firdaus Yuni Dharta, Hery Purwosusanto, Hefri Yodiansyah, Rahmi Setiawati, Aprilinda Aprilinda

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