Over the past two decades, the digital environment has evolved from a mere space for communication and information exchange into an autonomous infrastructure of social life. Within this environment, political attitudes are shaped, social movements emerge, economic expectations are constructed, and cultural codes are transformed. Social networks, messaging platforms, and other open communication channels now function not simply as tools for transmitting messages, but as complex socio-technical systems capable of exerting large-scale influence on collective consciousness and behavior. In the context of accelerating digitalization, national security has acquired fundamentally new dimensions. Alongside traditional military, economic, and political challenges, states increasingly confront risks originating within the information sphere. These risks manifest themselves in coordinated disinformation campaigns, processes of radicalization, manipulative steering of public opinion, artificial amplification of protest activity, and cross-border informational interventions capable of undermining institutional stability. A distinctive feature of contemporary digital threats lies in their latent and distributed character. Influence is rarely exerted directly; rather, it is constructed through a system of recurring signals, symbols, narratives, and behavioral patterns that gradually become embedded in public discourse. These elements may be conceptualized as markers of mass influence—stable indicators reflecting the presence of either orchestrated or spontaneous processes of impact within the digital environment. Their identification and modeling require an interdisciplinary approach integrating communication theory, sociology, psychology, criminology, network theory, and big data analytics. Modeling markers of mass influence implies a shift from descriptive observation to the development of systemic forecasting instruments. It involves the construction of conceptual and mathematical models capable of detecting early signals of informational waves, assessing their escalation potential, and evaluating their implications for societal resilience. In this regard, particular importance is attributed to platform algorithms, network community structures, content dissemination dynamics, and the role of automated accounts. The open nature of contemporary communication spaces further complicates the task of safeguarding national interests. While transparency and accessibility foster civic engagement and democratic development, the same openness facilitates the coordination of destabilizing campaigns, the dissemination of manipulative narratives, and the mobilization of vulnerable groups into radical activities. The balance between preserving digital freedoms and ensuring national security constitutes one of the central dilemmas of modern statehood. The relevance of this monograph stems from the necessity to establish a coherent theoretical framework capable of systematizing approaches to digital threat analysis through the lens of mass influence markers. The study seeks to develop an integrated model combining qualitative and quantitative methodologies, as well as to propose instruments for the early detection of risks within social networks, messaging applications, and other open communication platforms. The primary objective of this work is to conceptualize theories of modeling markers of mass influence in the digital sphere and to determine their significance for national security systems. To achieve this aim, the research examines theoretical foundations of network analysis, mechanisms of algorithmic content amplification, semantic indicators of radicalization, user behavioral patterns, and institutional countermeasures against information-based attacks. Accordingly, this monograph is designed to formulate a scientifically grounded methodology capable of enhancing understanding of mass influence processes in digital environments and strengthening societal and state resilience against emerging forms of informational impact.
Нартай et al. (Thu,) studied this question.