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asgari, M., Keyhanian, S. (2024). The Identification and Prioritization of Key AI Functionalities in Various Stages of Social Crisis Management. Electronic and Cyber Defense, 13(50), 7-53.
mahmoud asgari; Sina Keyhanian. "The Identification and Prioritization of Key AI Functionalities in Various Stages of Social Crisis Management". Electronic and Cyber Defense, 13, 50, 2024, 7-53.
asgari, M., Keyhanian, S. (2024). 'The Identification and Prioritization of Key AI Functionalities in Various Stages of Social Crisis Management', Electronic and Cyber Defense, 13(50), pp. 7-53.
asgari, M., Keyhanian, S. The Identification and Prioritization of Key AI Functionalities in Various Stages of Social Crisis Management. Electronic and Cyber Defense, 2024; 13(50): 7-53.

The Identification and Prioritization of Key AI Functionalities in Various Stages of Social Crisis Management

فصلنامه پژوهش های حفاظتی امنیتی
Volume 13, Issue 50, August 2024, Pages 7-53    PDF (8.11 M)
Document Type: Original Article
Authors
mahmoud asgari* 1; Sina Keyhanian2
1Supreme National Defense University
2Assistant Professor at Supreme National Defense University
Receive Date: 25 May 2024,  Revise Date: 19 June 2024,  Accept Date: 05 July 2024 
Abstract
Social crises, due to their sudden nature and complexity, have become a serious challenge to national security. Managing these crises requires access to diverse and extensive data as well as rapid processing capabilities that exceed human capacity alone. Artificial intelligence, as a disruptive technology, can play a crucial role in predicting, analyzing, and swiftly responding to social crises. This paper identifies and prioritizes AI functionalities in managing social crises with an emphasis on national security. The research methodology includes a systematic literature review and the use of the fuzzy TOPSIS model to rank these functionalities across the five stages of crisis management. Results indicate that the “prediction” stage holds the highest importance in social crisis management, as early identification and precise analysis at this stage can prevent crisis escalation and reduce public dissatisfaction. At this stage, predicting social unrest is the primary functionality, playing a fundamental role in effective crisis control. Additionally, attention to the “rapid response and emergency management” and “evaluation and learning” stages highlights the importance of prompt reaction and continuous learning from experiences to enhance crisis management strategies. The findings of this study could serve as a foundation for strategic changes in social crisis management and national security, aiding decision-makers in making smarter and more effective decisions during crises.
References
  1. Abboodi, B., Pileggi, S. F., & Bharathy, G. (2023). Social Networks in Crisis Management: A Literature Review to Address the Criticality of the Challenge. Encyclopedia, 3(3), 1157-1177.
  2. Adhikari, Anup. Soh, Leen-Kiat. Joshi, Deepti. Samal, Ashok. Werum, Regina. (2023). "Agent Based Modeling of the Spread of Social Unrest Using Infectious Disease Models". ACM Transactions on Spatial Algorithms and Systems. Vol 9. Issue 3. Pp 1 - 31
  3. Akers, J. Bansal, G. Cadamuro, G. Chen, C. Chen, Q. Lin, L. Mulcaire, P. Nandakumar, R. Rockett, M. Simko, L. Toman, J. Wu, T. Zeng, E. Zorn, B. Roesner, F. (2018). Technology-Enabled Disinformation: Summary, Lessons, and Recommendations. Technical Report UW-CSE. Available at https://arxiv.org/abs/1812.09383
  4. Aronson, J. E., Liang, T. P., & MacCarthy, R. V. (2005). Decision support systems and intelligent systems (Vol. 4). Upper Saddle River, NJ, USA: Pearson Prentice-Hall.
  5. Assenmacher, D. Clever, L. Frischlich, L. Quandt, T. Trautmann, H. Grimme, C. (2020) "Demystifying social bots: On the intelligence of automated social media actors". Social Media & Society. 6 (3).
  6. Bahrami, Mohsen. Findik, Yasin. Bozkaya, Burcin. Balcisoy, Selim. (2018). “Twitter Reveals: Using Twitter Analytics to Predict Public Protests”. Available at: https://arxiv.org/abs/1805.00358
  7. Baletic, Katarina. (2024). "Serbian Authorities Use High-Tech Surveillance to Monitor Opponents". https://balkaninsight.com/2024/06/28
  8. Bauer, M., & Erixon, F. (2020). Europe''s quest for technology sovereignty: Opportunities and pitfalls (No. 02/2020). ECIPE Occasional Paper.
  9. Benaben, F., Fertier, A., Montarnal, A., Mu, W., Jiang, Z., Truptil, S.,... & Lamothe, J. (2020). An AI framework and a metamodel for collaborative situations: Application to crisis management contexts. Journal of Contingencies and Crisis Management, 28(3), 291-306.
  10. Betts, R. K. (2010). Surprise attack: Lessons for defense planning. Brookings Institution Press.
  11. Bontridder N, Poullet Y. (2021). "The role of artificial intelligence in disinformation". Data & Policy. No 3. pp 1-32.
  12. Burmeister, E., and L. M. Aitken. 2012. "Sample size: How many is enough?" Australian Critical Care 25 (4): 271-274.
  13. Byman, Daniel L. Gao, Chongyang. Meserole, Chris. Subrahmanian, V.S. (2023). "Deepfakes and international conflict". https://www.brookings.edu/articles/deepfakes-and-international-conflict/
  14. Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, 41(7), e13406. https://doi.org/10.1111/exsy.13406
  15. Clareke, Ronald V & Eck, John E. (2009). Crime Analysis for Problem-Solvers: In 60 Small Steps. Washington: U.S: Department of Justice.
  16. Dangwal, Ashish. (2024). "China To Build Earth’s Largest Surveillance Network That Can Monitor Its Lunar Assets". https://www.eurasiantimes.com/china-to-build-earths-largest-surveillance-network
  17. Davis, R. C. Henderson, N.J. & Merrick, C. (2003). “Community Policing: Variations on the Western Model in the Developing World”. Police Practice and Research. Vol. 4. No. 3.
  18. De Bruijn, H., & Ten Heuvelhof, E. (2018). Management in networks. Routledge.
  19. Ercan, M., G. Özkan, T. D. Çalişkan, and G. Özkan. 2022. "Examination of a Hazardous Waste Disposal Plant in the Context of Occupational Health and Safety. WSEAS Transactions on Computers, 21."
  20. Feldstein, Steven. (2019). The Global Expansion of Al Surveillance. Washington. Carnegie Endowment for International Peace
  21. Festré, A. Garrouste, P. (2015). "The Economics of attention: A history of economic thought perspective". History, Methodology, Philosophy. 5 (1). pp 3–36
  22. Galla, D., & Burke, J. (2018). Predicting Social Unrest Using GDELT. IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition. pp.103-116
  23. Goldstein, Herman. (1979). "Improving Policing: A Problem-oriented Approach". Crime and Delinquency. 24
  24. Grill, G. (2021). Future protest made risky: Examining social media based civil unrest prediction research and products. Computer Supported Cooperative Work (CSCW), 30(5), 811-839.
  25. Hatami-Marbini, A., and F. & Kangi. 2017. "An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange." Applied Soft Computing (52): 1084-1097.
  26. IEP (Institute for Economics and Peace). (2023). Global Peace Index 2023: Measuring peace in a complex world. https://www.economicsandpeace.org/report/global-peace-index-2023.
  27. (2024). Misuse of artificial intelligence in elections. Shodh Sari, 3(4).
  28. Joshi, Naveen. (2021). "Using AI to predict social unrest and geopolitical events in a volatile region". https://www.linkedin.com/pulse/using-ai-predict-social-unrest-geopolitical-events-volatile-joshi
  29. Kacprzak, D. 2019. "A doubly extended TOPSIS method for group decision making based on ordered fuzzy numbers." Expert Systems with Applications (116): 243-254.
  30. Lamo, Calo. (2018). "Regulating Bot Speech". UCLA Law Review.
  31. Li, G., Wang, J., & Wang, X. (2023). Construction and path of urban public safety governance and crisis management optimization model integrating artificial intelligence technology. Sustainability, 15(9), 7487.
  32. Macis, Luca. Tagliapietra, Marco. Meo, Rosa. Pisano, Paola. (2024). "Breaking the trend: Anomaly detection models for early warning of socio-political unrest". Technological Forecasting and Social Change. Vol 206.
  33. Maras, M. H. Alexandrou, A. (2018). “Determining authenticity of video evidence in the age of artificial intelligence and in the wake of deepfake videos”. The International Journal of Evidence & Proof. vol. 23. no. 3. pp. 255–262.
  34. Meaker, Morgan. (2020). "Marseille’s fight against AI surveillance". codastory.com/authoritarian-tech/ai-surveillance-france-crime.
  35. Miracola, S. (2019). How China Uses Artificial Intelligence to Control Society. Italian Institute for International Political Studies.
  36. Mitroff, I. I. (2000). Managing crises before they happen: What every executive and manager needs to know about crisis management. AMACOM/American Management Association
  37. Morchhale, Y., Jain, A., Dixit, A., Shrivastava, G. K., & Mukati, S. (2024). Ethical considerations in artificial intelligence: Addressing bias and fairness in algorithmic decision-making. International Journal of Scientific Research in Engineering and Management (IJSREM), 8(4), 1-6. https://doi.org/10.55041/IJSREM31693
  38. Mortensen, Mette. (2024). Sneaking AI through the back door: constructing the identity of Capitol Hill rioters through social media images and facial recognition technologies. Information, Communication & Society. 1–17.
  39. Mourad Fahmi, Bassant. Farouk, Mohamed Adel. (2024). "Deepfake Technology: A Comprehensive Analysis of its Societal Implications and Impact. Arab Journal of Media & Communication Research.
  40. OECD (Organisation for Economic Co-operation and Development). (2023). OECD forecasts during and after the financial crisis.
  41. Oladele, Temidayo Michael. Ayetiran, Eniafe Festus. (2023). "Social Unrest Prediction Through Sentiment Analysis on Twitter Using Support Vector". Open Information Science. 7 (1). Pp 1-11.
  42. Olvera, Abi. (2024). "How AI surveillance threatens democracy everywhere". Bulletin of the Atomic Scientists.
  43. Quantum, Sophia. (2024). AI in Crisis Management: Automated Content Strategies for Rapid Response. https://medium.com/@SophiaQuantum/ai-in-crisis-management-automated-content-strategies-for-rapid-response.
  44. Rajić, M. N., Maksimović, R. M., & Milosavljević, P. (2023). Emergency Planning and Disaster Recovery Management Model in Hospitality—Plan-Do-Check-Act Cycle Approach. Sustainability, 15(7), 6303.
  45. Rosenthal, U., Boin, A., & Comfort, L. K. (2001). Managing crises: Threats, dilemmas, opportunities. Charles C Thomas Publisher.
  46. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.
  47. Sànchez-Marrè, M. (2022). Intelligent Decision Support Systems. In Intelligent Decision Support Systems (pp. 77-116). Cham: Springer International Publishing.
  48. Saputro, A. T., Naim, Y., Yani, D.P, Supriyanto, A., & Sumitra, T. (2024). Unleashing the Potential of Artificial Intelligence: Advancements, Applications, and Ethical Considerations. Global International Journal of Innovative Research, 2(6). https://doi.org/10.59613/global.v2i6.204
  49. Sasikala, D. Premalatha, K. (2018). “Social Network Analysis for Prediction of Civil Unrest: A Review”. International Journal of Pure and Applied Mathematics. Vol 119. No. 16.
  50. Seagrave, J. (1996). "Defining Community Policing". American Journal of Police. 15 (2).
  51. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS quarterly, 553-572.
  52. Sørensen, J. L., Berlin, J., Nielsen, L. R., & Carlstrøm, E. (2022). Emergency, crisis, and risk management: Current perspectives on the development of joint risk mitigation, preparedness and response efforts. Frontiers in Communication, 7, 1044231.
  53. Sprenkamp, K., Zavolokina, L., Angst, M., & Dolata, M. (2023). Data-driven governance in crises: Topic modelling for the identification of refugee needs. In 24th Annual International Conference on Digital Government Research - Together in the unstable world: Digital government and solidarity (DGO 2023) (pp. 1-11). ACM. https://doi.org/10.1145/3598469.3598470
  54. Thapa, J. (2024). The impact of artificial intelligence on elections. International Journal of Future Multidisciplinary Research (IJFMR), 6(2). https://doi.org/10.36948/ijfmr.2024.v06i02.17524
  55. Tools for Social Media Crisis Management. (2024). https://www.fastercapital.com/content/Tools-for-Social-Media-Crisis-Management.html
  56. Walorska, A. (2020). Deepfakes and Disinformation. Friedrich Naumann Foundation for Freedom. Available at: https://fnf-europe.org/wp-content/uploads/2020/06/fnf_deepfakes_broschuere_en_web.pdf
  57. Ware, Jacob. Busch, Ella. (2023). "The Weaponization of Deepfakes: Digital Deception on the Far-Right". Policy Brief. https://www.icct.nl/publication/weaponization-deepfakes-digital-deception-far-right.
  58. World Bank. (2021). Economic impacts of social unrest.
  59. Yuan, Shaoyu. (2024). "China’s AI strategy all about serving the state". The conversation.
  60. Yusuf, H., M. I. Kemal, M. Nadhira Riska, P. Pradnya Paramita, and S. & Hidayatullah. 2019. "Evaluation of Alternative Payment Methods Effectiveness in Universitas Indonesia Main Gate using Combination of Fuzzy AHP and Fuzzy TOPSIS."
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