Keywords
How to Cite
Abstract
Aim: In the contemporary context of a constantly evolving security environment, the accelerating impact of technological advances and global interconnectivity, innovative solutions that can enhance security by engaging members of society are becoming increasingly valuable. Solutions based on the crowdsourcing model are one such example. These solutions can be effective in specific areas of security-related activities by mobilising diverse groups within society and leveraging community collaboration. The method is not a recent innovation; however, the possibilities of cyberspace have brought to the fore the accessibility of members of society and the related research directions. The advantages of this method include such benefits as speed, cost-effectiveness and the broadening of human resources. However, challenges such as data security, data authenticity and data manipulation must also be considered. The aim of this paper is to draw attention to the security-related applicability of the crowdsourcing model, to highlight the specific aspects of the crowdsourcing model for security purposes, and to emphasise the importance of further research on the topic.
Methodology: For the research methodology of the study, the authors have chosen to analyse the international literature, after examining the general literature on the subject, moving on to examine security and national security-oriented application examples and aspects. The aim was not only to present case studies, but also to explore the advantages and disadvantages of crowdsourcing and the solutions proposed to address them.
Findings: A review of international examples and literature reveals that crowdsourcing solutions are gradually gaining ground in security thinking. It is already present in our daily lives in a number of areas, including cybersecurity, information gathering related to security incidents, and the channelling of specific expertise and capacities. However, alongside the advantages (human resources, cost-effectiveness, speed) that are clearly identifiable, there are also a number of drawbacks and limitations (the (data) security of the participants, or even the authenticity, manipulability and contamination of the data) and factors that make the application of the method more difficult, which makes the scientific study of the subject even more complex.
Value: In the Hungarian-language literature on security issues, there is a scarcity of resources on the method. With this study, the authors aim to bring the topic to the fore and to promote further scientific reflection and professional discourse on the subject.
References
Acker, A. (2018). Data craft: The manipulation of social media metadata. Data & Society Research Institute. https://datasociety.net/wp-content/uploads/2018/11/DS_Data_Craft_Manipulation_of_Social_Media_Metadata.pdf
Bakici, T. (2020). Comparison of crowdsourcing platforms from social-psychological and motivational perspectives. International Journal of Information Management, 52, 102097. https://doi.org/10.1016/j.ijinfomgt.2020.102121
Brabham, D. C. (2013). Crowdsourcing: A model for leveraging online communities. In A. Delwiche & J. J. Henderson (Eds.), The participatory cultures handbook (pp. 120–129). Routledge.
Dobák, I. (2025). Crowdsourcing for security in the age of hybrid threats. Security and Defence Quarterly. https://doi.org/10.35467/sdq/197309
Estellés-Arolas, E. (2022). Using crowdsourcing for a safer society: When the crowd rules. European Journal of Criminology, 19(4), 692–711. https://doi.org/10.1177/1477370820916439
Estellés-Arolas, E. & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189–200. https://doi.org/10.1177/0165551512437638
Feng, W., Yan, Z., Zhang, H., & Zeng, K. (2017). A survey on security, privacy, and trust in mobile crowdsourcing. IEEE Internet of Things Journal. https://ieeexplore.ieee.org/document/8080202
Franzoni, C. & Sauermann, H. (2014). Crowd science: The organization of scientific research in open collaborative projects. Research Policy, 43(1), 1–20. https://doi.org/10.1016/j.respol.2013.07.005
Garrigos-Simon, F. J. & Narangajavana-Kaosiri, Y. (2024). Crowdsourcing: A systematic literature review and future research agenda. European Journal of Studies in Management and Business, 30(1), 1–26. https://doi.org/10.32038/mbrq.2024.30.01
Gradecki, J. & Curry, D. (2017). Crowd-sourced intelligence agency: Prototyping counterveillance. Big Data & Society, 4(1), 1–13. https://doi.org/10.1177/2053951717693259
Harrison, S. & Johnson, P. (2019). Challenges in the adoption of crisis crowdsourcing and social media in Canadian emergency management. Government Information Quarterly, 36(3), 501–509. https://doi.org/10.1016/j.giq.2018.09.009
Havas, C., Resch, B., Francalanci, C., Pernici, B., & Scalia, G. (2017). E2mc: Improving emergency management service practice through social media and crowdsourcing analysis in near real time. Sensors, 17(12), 2766. https://doi.org/10.3390/s17122766
Herhkovitz, S. (2020). Crowdsourced intelligence (Crosint): Using crowds for national security. The International Journal of Intelligence, Security, and Public Affairs, 22(1), 42–55. https://doi.org/10.1080/23800992.2020.1744824
Hossain, M. & Kauranen, I. (2015). Crowdsourcing: A comprehensive literature review. Strategic Outsourcing: An International Journal, 8(1), 2–22. https://doi.org/10.1108/SO-12-2014-0029
Howe, J. (2006, June 1). The rise of crowdsourcing. Wired. https://www.wired.com/2006/06/crowds/
Hui, J. Y. (2015). Crowdsourcing for national security. S. Rajaratnam School of International Studies. http://www.jstor.org/stable/resrep05853
Khandpur, R. P., Ji, T., Jan, S., Wang, G., & Lu, C. T. (2017). Crowdsourcing cybersecurity: Cyber attack detection using social media. In Proceedings of the 2017 ACM on Web Science Conference (pp. 263–272). https://doi.org/10.1145/3132847.3132866
Lamprou, E. & Antonopoulos, N. (2021). Crowdsourcing as a tool against misinformation: The role of social media and user-generated content in overturning misinformation during the Greek Covid-19 pandemic. In Adapt to Survive: The Role of Social Media, Sharing and Communication to Ameliorate This World (pp. 13–23). Communication Institute of Greece. https://coming.gr/wp-content/uploads/2021/12/1_1_2021_Adapt-to-survive_Book_conf-proceedings_COMinG.pdf
Latvian Institute of International Affairs, & Riga Stradins University. (2016). Internet trolling as a hybrid warfare tool: The case of Latvia. NATO Strategic Communications Centre of Excellence. https://stratcomcoe.org/publications/internet-trolling-as-a-hybrid-warfare-tool-the-case-of-latvia/160
Liu, S. B. (2014). Crisis crowdsourcing framework: Designing strategic configurations of crowdsourcing for the emergency management domain. Computer Supported Cooperative Work (CSCW), 23(4–6), 389–443. https://doi.org/10.1007/s10606-014-9204-3
McCuen, J. J. (2008). Hybrid wars. Military Review, 88(2), 107–113.
Nhan, J., Huey, L., & Broll, R. (2017). Digilantism: An analysis of crowdsourcing and the Boston Marathon bombings. British Journal of Criminology, 57(2), 341–361. https://doi.org/10.1093/bjc/azv118
Ren, Y., Liu, W., Liu, A., Wang, T., & Li, A. (2022). A privacy-protected intelligent crowdsourcing application of IoT based on the reinforcement learning. Future Generation Computer Systems, 125, 1–12. https://doi.org/10.1016/j.future.2021.03.011
Schenk, E. & Guittard, C. (2011). Towards a characterization of crowdsourcing practices. Journal of Innovation Economics & Management, 7(1), 93–107. https://doi.org/10.3917/jie.007.0093
Shen, X. L., Lee, M. K. O., & Cheung, C. M. K. (2014). Exploring online social behavior in crowdsourcing communities: A relationship management perspective. Computers in Human Behavior, 40, 144–151. https://doi.org/10.1016/j.chb.2014.07.048
Shmueli, B., Fell, J., Ray, S., & Ku, L. W. (2021). Beyond fair pay: Ethical implications of NLP crowdsourcing. arXiv preprint arXiv:2104.10097. https://doi.org/10.18653/v1/2021.naacl-main.295
Spiliotopoulou, L., Charalabidis, Y., & Loukis, E. N. (2014). A framework for advanced social media exploitation in government for crowdsourcing. Transforming Government: People, Process and Policy, 8(4), 545–568. https://doi.org/10.1108/TG-01-2014-0002
Sridhar, K. & Ng, M. (2021). Hacking for good: Leveraging HackerOne data to develop an economic model of bug bounties. Journal of Cybersecurity, 7(1), tyab007. https://doi.org/10.1093/cybsec/tyab007
Vukovic, M., Lopez, M., & Laredo, J. (2009, November). PeopleCloud for the globally integrated enterprise. In Service-Oriented Computing. ICSOC/ServiceWave 2009 Workshops (pp. 109–114). Springer. https://doi.org/10.1007/978-3-642-16132-2_10
Wittau, J. & Seifert, R. (2024). Metadata analysis of retracted fake papers in Naunyn-Schmiedeberg’s Archives of Pharmacology. Naunyn-Schmiedeberg's Archives of Pharmacology, 397(6), 3995–4011. https://doi.org/10.1007/s00210-023-02850-6
Xia, H. & McKernan, B. (2020). Privacy in crowdsourcing: A review of the threats and challenges. Computer Supported Cooperative Work (CSCW), 29, 263–301. https://doi.org/10.1007/s10606-020-09374-0
Xu, Z., Liu, Y., Yen, N. Y., Mei, L., & Luo, X. (2016). Crowdsourcing based description of urban emergency events using social media big data. IEEE Transactions on Big Data, 2(3), 324–335. https://ieeexplore.ieee.org/document/7381652
Yang, K., Zhang, K., Ren, J., & Shen, X. (2015). Security and privacy in mobile crowdsourcing networks: Challenges and opportunities. IEEE Communications Magazine, 53(8), 75–81. https://doi.org/10.1109/MCOM.2015.7180511
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2025 Academic Journal of Internal Affairs