MILITARY MANAGEMENT QUARTERLY

MILITARY MANAGEMENT QUARTERLY

From preemptive flowmaking to communication blockage: A computational analysis of Platform X algorithmic governance in geopolitical crises

Document Type : Research Paper

Authors
Bahareh Moqarnia 1
Mahnaz Amirpour 2
Samad Abedini 3
1 Department of Communication Sciences, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 Department of Social Sciences, Islamic Azad University, Quchan Branch, Quchan, Iran
3 Department of Social Sciences, Islamic Azad University, Khalkhal Branch, Khalkhal, Iran
10.22034/iamu.2026.2091448.3270
Abstract
In contemporary geopolitical crises, digital platforms have transformed from neutral communication tools into active infrastructures of “algorithmic governance” and the main battlefield of narrative warfare. During the Iran-Israel military tensions, Platform X played a pivotal role in shaping public opinion. Applying a computational social science approach, this study dissects how algorithms function in shaping the flow of information. Accordingly, 30,000 enriched data records (filtered from 120,000 raw data) were analyzed in three time phases: pre-crisis (T0), crisis peak (T1), and post-crisis (T2) using natural language processing, sentiment analysis, and network analysis techniques.
The computational findings show that in the pre-crisis phase, the platform prepared the network atmosphere for conflict by applying “preemptive salience” and marginalizing pacifist narratives. At the height of the crisis and at the same time as human users were blocked from communicating, machine actors (bots) filled the agency vacuum with a strategy of space saturation and maximized the “digital silence spiral” by systematically amplifying the feeling of anger (53% of content). In the post-crisis phase, algorithms returned authority to human influencers by creating guided bipolarity to give legitimacy and a lasting identity to machine-generated narratives. The results of this study prove that gatekeeping in the era of big data has changed from human-centered content management to the engineering of emotional and algorithmic infrastructures, and artificial intelligence has become the main architect of public perception with its invisible management of data.
Keywords
Algorithmic governance
computational social science
narrative warfare
spiral of digital silence
big data
Subjects
·       حدادی، علیرضا (۱۴۰۳). چارچوبی نوین برای سنجش سرمایه اجتماعی نظام‌های سیاسی با استفاده از روش علوم اجتماعی محاسباتی.  فصلنامه علوم اجتماعی، ۳۱(105)، ۴۷-۹۵. doi: 10.22054/qjss.2025.81459.2821
·       رستگار، ی. (علی)، خانیکی، ه. (هادی)، و رحمانی‌زاده دهکردی، س. (حمیدرضا). (۱۴۰۳). تحلیل داده‌های حذف‌شده کاربران منتسب به ایران توییتر در بازه انتخابات ریاست جمهوری ۲۰۲۰ آمریکا. مطالعات فرهنگی و ارتباطات، ۲۰(۷۴)، ۱۱-۴۴. https://doi.org/10.22034/jcsc.2022.550364.2546
·       عبداللهیان، ح.، و کرمانی، ح. (۱۳۹۹). همگان‌های شبکه‌ای در توئیتر فارسی؛ تحلیل خوشه‌های شبکه‌ای در توئیتر فارسی در انتخابات ریاست جمهوری سال ۱۳۹۶. مطالعات رسانه‌های نوین، ۶(۲۲)، ۷۰–۱۰۰. https://doi.org/10.22054/nms.2021.44855.784
·       عبدیان، ص. (صبا)، حسین‌زاده شهری، م. (معصومه)، و خدیور، آ. (آمنه). (۱۴۰۲). تجزیه و تحلیل بیبلیومتریک داده‌های بزرگ و پتانسیل آن برای ایجاد و اخذ ارزش. مجله ایرانی مطالعات مدیریت، ۱(۱)، ۱-۲۴.
·       لبافی، س. (کیاء)، و نظریان. (۱۴۰۱). شناسایی تأثیرات دروازه‌بانی الگوریتمی در پلتفرم‌های رسانه‌ای بر منافع عمومی کاربران. پژوهش‌های ارتباطی، ۲۹(۱۱۰)، ۴۳-۶۷.
·       نصری، س. ا.، حسن‌پور، ح.، آقاابراهیمی، ب.، و سپنجی، ا. ع. (۱۴۰۲). ارزیابی عملکرد رسانه‌های خبری فارسی‌زبان در شبکه اجتماعی توییتر با استفاده از روش ترکیبی داده‌کاوی، تحلیل شبکه‌ای (ANP) و تحلیل پوششی داده‌ها (DEA). مطالعات مدیریت راهبردی دفاع ملی، ۷(۲۵)، ۲۶۷-۳۰۴.
 
·       Adewusi, A. A., Olabiyisi, S. O., Omidiora, E. O., & Odetunmibi, O. A. (2025). Big data-driven business intelligence for competitive advantage: A comparative analysis. Journal of Information Systems and Technology Management, 22(1), 12–34. https://doi.org/10.1016/j.jistm.2025.01.002
·       Alodat, A. M., Al‑Qora’n, L. F., & Abu Hamoud, M. (2023). Social media platforms and political participation: A study of Jordanian youth engagement. International Journal of Communication, 17, 4402–4424.
·       Bandy, J. (2021). Problematic machine behavior: A systematic literature review of algorithm audits. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), Article 1. https://doi.org/10.1145/3449103
·       Blumenstock, J. E., Chi, G., & Tan, X. (2023). Migration and the value of social networks. American Economic Review, 113(8), 2115–2148.
·       Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313-7318. https://doi.org/10.1073/pnas.1618923114
·       Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51-58. https://doi.org/10.1111/j.1460-2466.1993.tb01304.x
·       Fan, C., Wu, F., & Mostafavi, A. (2025). Dynamics of collective information processing for risk encoding in social networks during crises. International Journal of Disaster Risk Reduction, 122, 105451.
·       Faus, M., Rebollo, C., López, D., & Ortega, F. (2025). AI and big data in public health campaigns: A systematic review. Health Communication Research, 18(3), 211–229. https://doi.org/10.1016/j.hcr.2025.03.005
·       Gillespie, T. (2010). The politics of ‘platforms’. New Media & Society, 12(3), 347-364. https://doi.org/10.1177/1461444809342738
·       Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media technologies: Essays on communication, materiality, and society (pp. 167-194). MIT Press.
·       Hu, Y., & Wang, J. (2021). How do people describe locations during a natural disaster: An analysis of tweets from Hurricane Harvey. Leibniz International Proceedings in Informatics, 185, Article 6. https://doi.org/10.4230/LIPIcs.GISCIENCE.2021.6
·       Kuai, J., Brantner, C., Karlsson, M., Van Couvering, E., & Romano, S. (2025). AI chatbot accountability in the age of algorithmic gatekeeping: Comparing generative search engine political information retrieval across five languages. new media & society, 14614448251321162.
·       Nieuwenhuizen, E. (2025). Trust and transparency in algorithmic governance: a multi-level framework. In Handbook on Trust in Public Governance (pp. 116-135). Edward Elgar Publishing.
·       Noelle-Neumann, E. (1974). The spiral of silence: A theory of public opinion. Journal of Communication, 24(2), 43-51. https://doi.org/10.1111/j.1460-2466.1974.tb00367.x
·       Okonkwo, I., & Awad, H. A. (2024). The role of social media in enhancing communication and collaboration in business. International Journal of Business Communication, 61(1), 75–96
·       Olaboye, S., Zhang, T., & Chen, H. (2024). Social media mining for early epidemic detection in Southeast Asia. Public Health Informatics Journal, 29(4), 387–402. https://doi.org/10.1016/j.phij.2024.11.003
·       Satish Kumar, M., Singh, V. P., & Agarwal, R. (2024). Network centrality and clustering in big data: A graph mining approach. Journal of Computational Social Systems, 11(2), 66–84. https://doi.org/10.1002/css.2024.00125
·       Shahmerdanova, R. (2024). Artificial intelligence in translation: Challenges and opportunities. Acta Globalis Humanitatis et Linguarum, 2(1), 23‑37. https://doi.org/10.69760/aghel.02500108
·       Speckhard, A., Shajkovci, A., & Ellenbogen, A. (2024). Beyond volume: Quality and contextual relevance in big data. International Journal of Data Ethics and Strategy, 5(1), 13–28. https://doi.org/10.1016/j.ijdes.2024.01.001
·       Woolley, S. C., & Howard, P. N. (2016). Political communication, computational propaganda, and autonomous agents—Introduction. International Journal of Communication, 10, 4882-4890.
·       Woolley, S. C., & Howard, P. N. (2018). Computational propaganda: Political parties, politicians, and political manipulation on social media. Oxford University Press.