Early detection of unrest signals in social media A case study of Telegram regarding the protests in 1397 SH Iran

Document Type : Original Article

Authors

1 Department of information technology, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Professor, School of Management, University Of Tehran

3 Assistant Proffessor in AI and Robotics, University of Tehran

Abstract

Since the advent of the mobile data network, Iran has more than 35 million social media users. Developing social media among the general public (with a penetration rate of 49% in Iran) and their diverse functions can be the starting point for using social sensing. In this case, users provide data, like a sensor, for analysis. One of the applications of social sensing is crisis management. To achieve this goal, big data collection as a defense system can reduce the human, economic and social costs of crises and events, and can be used as a tool to raise situational awareness and enhance national security. The data set of the present study was collected based on the crawling and text mining of verbal violence in one million and four hundred thousand general Persian channels of Telegram messenger in 1397 SH and after refinement, it was modeled based on the time series of the moving average. To identify crisis signals in this model, the oscillator following the momentum trend (which is mostly used in financial analyses) and the moving average of divergence convergence (MACD) are analyzed. This is the first time in the computational social sciences that this tool has been used to predict security crises and political events and to allow government surveillance.
The research findings confirm that at least six social protest in the country in 1397 SH were identifiable and manageable before the event. In addition, a system that can use such analyses in social media big data in real-time would have the necessary efficiency to issue an early warning and to measure the political and security risks of society.

Keywords


  • فهرست منابع ومآخذ

    الف. منابع فارسی

    • جان مورفی (1399)، تحلیل تکنیکال در بازارهای سرمایه­، ترجمۀ،کامیار فراهانی فرد، رضا قاسمیان لنگرودی­، تهران: چالش
    • سهرابی، محمد؛ مسلمی، سیاوش (1394)، راهبردهای شبکه‌های اجتماعی مجازی برای کنترل بحران امنیتی،فصلنامۀ علمی امنیت مل ی 5(15), 9-32
    • شجاع مؤدب، حمیدرضا؛ حسینی­تاش، سید علی؛ آقایی، محسن؛ امیری مقدم، رضا (1399)؛ ارائۀ مدل تحول در چرخه‌های ظهور بحران‌های سیاسی - امنیتی، متأثر از فضای سایبر، فصلنامۀ علمی امنیت ملی،­10(35), 179-204.
    • عباسی، حسن؛ شریعت، جهانگیر (1397)، کارکرد سیاسی شبکه‌های اجتماعی مجازی،فصلنامۀ علمی امنیت ملی 8(30) 127-150.

     

    ب. منابع انگلیسی

    • Chang-Won Kim, Wi Sung Yoo, Hyunsu Lim, Ilhan Yu, Hunhee Cho, Kyung-In Kang, (2018) Early-warning performance monitoring system (EPMS) using the business information of a project,International Journal of Project Management,Volume 36, Issue 5 ,Pages 730-743.
    • Wang, B. K. Szymanski, T. Abdelzaher, H. Ji and L. Kaplan, "The Age of Social Sensing," in Computer, vol. 52, no. 1, pp. 36-45, Jan. 2019, doi: 10.1109/MC.2018.2890173.
    • Kruspe, Anna & Kersten, Jens & Stein, Benno & Klan, Friederike. (2018). Classification of Incident-related Tweets: Tackling Imbalanced Training Data using Hybrid CNNs and Translation-based Data Augmentation.
    • MacAvaney S, Yao H-R, Yang E, Russell K, Goharian N, Frieder O (2019) Hate speech detection: Challenges and solutions. PLoS ONE 14(8): e0221152.
    • Maharaj, E. A., D'Urso, P., & Caiado, J. (2019). Time series clustering and classification.  CRC Press
    • Molaei, M. Kargari, M. SheikhMohammady, A. Akramizadeh (2019) Deterrence Model in Cyberspace Based on Bayesian Belief Attack Graph by using Risk Creating Payoff Function,Journal of Electronical & Cyber Defence ,Vol. 7, No. 1,P25-38
    • Murphy, J. J., & Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York: New York Institute of Finance.
    • Mustafa, Raza & Nawaz, M. Saqib & Ferzund, Javed & Lali, Muhammad Ikram & Shahzad, Basit & Fournier Viger, Philippe. (2017). Early Detection of Controversial Urdu Speeches from Social Media. Data Science and Pattern Recognition. 1. 26-42.
    • Nazir, F., Ghazanfar, M.A., Maqsood, M. et al. Social media signal detection using tweets volume, hashtag, and sentiment analysis. Multimed Tools Appl 78, 3553–3586 (2019).
    • Peiwan Wang, Lu Zong, Ye Ma,An(2020) integrated early warning system for stock market turbulence,Expert Systems with Applications,Volume 153.
    • Phillips, Lawrence & Dowling, Chase & Shaffer, Kyle & Hodas, Nathan & Volkova, Svitlana. (2017). Using Social Media to Predict the Future: A Systematic Literature Review.
    • Schewe, Klaus-Dieter & Singh, Neeraj. (2019). Model and Data Engineering 9th International Conference, MEDI 2019, Toulouse, France, October 28-31
    • Sofyan Sufri, Febi Dwirahmadi, Dung Phung, Shannon Rutherford, (2020) A systematic review of Community Engagement (CE) in Disaster Early Warning Systems (EWSs), Progress in Disaster Science, Volume 5.
    • Stieglitz, S., Bunker, D., Mirbabaie, M., & Ehnis, C. (2017a). Sense-Making in Social Media During Extreme Events. Journal of Contingencies and Crisis Management (JCCM).
    • Terence C. Mills. (2019). Applied Time Series Analysis, Academic Press, Pages 1-12
    • Varol, O., Ferrara, E., Menczer, F. et al. (2017) Early detection of promoted campaigns on social media. EPJ Data Sci. 6, 13.
    • Wu Jiekang, Wu Zhijiang, Mao Xiaoming, Wu Fan, Tang Huiling, Chen Lingming(2020),Risk early warning method for distribution system with sources-networks-loads-vehicles based on fuzzy C-mean clustering,Electric Power Systems Research,Volume 180.
    • Yi Chai, Hao Luo, Qingpeng Zhang, Qijin Cheng, Carrie S.M. Lui, Paul S.F. Yip,Developing an early warning system of suicide using Google Trends and media reporting,Journal of Affective Disorders,Volume 255,2019,Pages 41-49.
    • Zhang, X., Zhong, Q., Zhang, R., & Zhang, M. (2020). People-centered early warning systems in China: A bibliometric analysis of policy documents. International journal of disaster risk reduction: IJDRR, 51, 101877.