Modeling Unstable Critical States and Activity Waves in Social Network Communications

The article is devoted to computer simulation modeling of nonlinear processes in social communications with threshold and impulse effects when launching the activity of opposing communities. Popular modern means of online communication in the era of information society (messengers, the blogosphere, and social networking sites) are used to purposefully form a distorted information matrix of behavior. Not only is hidden advertising being promoted on blogs, but also campaigns are being conducted to disseminate informational outrage and content to shape destructive ideological narratives online. The coordinated deployment of fake news to influence behavior has been clearly demonstrated by the noisy campaign of psychological attacks against vaccines. During the coronavirus pandemic, anti-vaccination propaganda has rapidly intensified online. The injection of prepared messages about the terrible harm of vaccines spread across social networks in pulsed waves. Processes in online communities have become analogous to the pulsating activity of the coronavirus itself. Suddenly, these information waves would spread beyond the local online communities that generated them, once a certain critical mass of active participants in the dissemination of fake news had been reached. Extreme information processes are sharply activated after reaching a certain critical level of activity, but then fade away after the interest of the main audience decreases and the trend loses its relevance. The destructive influence of the advancement of waves of false and psychologically aggressive information on the behavior of the active part of society persists for years. To model the phenomena of social network disturbances, it is necessary to consider the role of critical states and threshold effects in the build-up of excitement on the network. It is relevant to consider models of critical explosive scenarios for the emergence of so-called “hype waves.” Unfortunately, due to psychology, people join a movement en masse if they see many of its participants around them, but this large number is easily created by artificial manipulation. Information noise is always present, but sometimes the influx of content into the network turns into an avalanche, causing secondary waves, but, like an epidemic, an avalanche of rapid spread can die down. For the purposes of predicting the spontaneous launch of hype waves, we propose modifying the equations of models with active critical equilibrium points. Levels and special states change the course of development of the situation in the information space and describe the transformation of content into viral information. Two methods for modeling the impact of critical states for different information environments are considered. Continuous equations have been modified based on the idea of including a threshold unstable value on the right side as a critically permissible saturation of the community with active trigger content, which immediately causes the formation of a wave in the information space. Modifications with a single threshold value in the delay equation for generating sharply damped oscillations are considered. Hybrid computational structures are proposed to describe the flexible adaptive transformation of the threshold level of some information saturation, which abruptly leads to an outbreak of the spread of injected information in the network community. The hybrid continuous-event model forms several unstable stationary states in the iteration dynamics, during the transitions between which the system behavior changes sharply. The new method allows us to describe crisis development scenarios in Withnetwork systems even with weak disturbance of the information space. The resulting models exhibit diverse behavior with the occurrence of bifurcations, the emergence of cycles and alternative attractors. The model requires an extension of behavior and a search for solutions with different properties to describe waves in network structures.