Abstract

As outlined in the UN Development Program the examination of human security implicates the analysis of different elements along seven domains: health security (linked to freedom of disease or infection); food security (complete access to food); economic security (relative to assure basic income); personal security (protect from violence and other forms of threats); political security (protection of fundamental human rights and freedoms); communal security (security of cultural identity) and environmental security (access to sa nitary water supply and other basic needs such as clean air). As the ongoing COVID-19 crisis has demonstrated, pandemics cause disruptions at different levels, and have negative social, economic and political impacts. They are thus serious human and national security threats.
Countries worldwide have to protect their communities against those natural and man-made crises that threaten these critical elements that maintain the stability of the state. Policy and decision-makers need thus to have at their disposal technological tools to gain insights about unfolding situations and assess the threat to national and international security in the presence of ensuing crises. Social media analytics can provide a powerful force multiplier in this endeavor.
The talk will provide an overview of an info-symbiotic data analytics framework for human security that can be used as decision support tool to analyse information dissemination through social media and predict the onset of escalating crisis situations and to prevent or mitigate both the onset and the impact. The robustness of the framework has been successfully demonstrated in several crises, including social unrests resulting from COVID-19 prevention measures (lockdowns). Future work and research challenges will also be outlined.

References

• Pedro Cardenas , Nikos Tziritas, Georgios Theodoropoulos, “Info-Symbiotic Systems for Global Governance: National Security and Pandemics”, Handbook of Dynamic Data Driven Applications Systems, Vol II, Springer, to appear.
• Pedro Cardenas Canto, Georgios Theodoropoulos, Boguslaw Obara, Ibad Kureshi and Ioannis Ivrissimtzis, “Big Data for National Security in the Era of COVID-19”, The International Conference on Computational Science, ICCS2021, Krakow, Poland, 16-18 June, 2021. To appear
• Pedro Cardenas-Canto, Georgios Theodoropoulos, Boguslaw Obara, Ibad Kureshi, “Unveiling Ideological Trends Through Data Analytics to Construe National Security Instabilities”, IEEE International Conference on Big Data 2020 (IEEE BigData 2020), Deviant Activities on Social Media, December 10-13, 2020.
• Pedro Cardenas-Canto, Boguslaw Obara, Ibad Kureshi, Georgios Theodoropoulos, “Analysing Social Media as a Hybrid Tool to Detect and Interpret likely Radical Behavioural Traits for National Security”, 2019 IEEE International Conference on Big Data (IEEE BigData 2019), 3rd Workshop on Human-in-the-loop Methods and Human-Machine Collaboration in BigData, Los Angeles, USA, Dec 9-12, 2019, DOI Bookmark: 10.1109/BigData47090.2019.9006259
• P. Cárdenas, G. Theodoropoulos and B. Obara, "Web Insights for National Security: Analysing Participative Online Activity to Interpret Crises," 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Milan, Italy, 2019, pp. 235-243, DOI Bookmark: 10.1109/ICCICC46617.2019.9146090.
• Pedro Cardenas-Canto, Boguslaw Obara, Ibad Kureshi, Georgios Theodoropoulos, “Defining an Alert Mechanism for Detecting likely threats to National Security”, 2018 IEEE International Conference on Big Data (IEEE BigData 2018), Seattle, WA, USA, Dec 10-13, 2018. DOI Bookmark: 10.1109/BigData.2018.8622569
• Pedro Cardenas-Canto, Georgios Theodoropoulos, Boguslaw Obara, Ibad Kureshi, “A Conceptual Framework for Social Movements Analytics for National Security” International Conference on Computational Science (ICCS 2018), Wuxi, China, 11-13 June, 2018. Shi Y. et al. (eds) Computational Science – ICCS 2018. ICCS 2018. Lecture Notes in Computer Science, vol 10860. Springer, DOI Bookmark: 10.1007/978-3-319-93698-7_23