The Impact of Regulation Threshold Heterogeneity on the Spread of Negative Emotions in Social Networks

Incorporating regulation threshold heterogeneity into the modeling of emotion diffusion provides critical insights into the spread of negative emotions within complex social networks. This study develops a threshold-based diffusion framework to systematically examine how individual differences in emotional response thresholds influence the spatiotemporal dynamics of negative emotion propagation. The proposed behavioral model assumes that individual thresholds follow a Gaussian distribution, capturing variations in emotional susceptibility across the network. Using edge-based compartmental theory, the model analyzes the propagation process under the influence of threshold heterogeneity. Through numerical simulations and empirical validation, the study demonstrates that increasing the average regulation threshold significantly raises the outbreak threshold and reduces the risk of large-scale emotional contagion. At the same time, heterogeneity in threshold distribution leads to complex nonlinear propagation behaviors, including both continuous and discontinuous phase transitions. These findings emphasize the importance of threshold distribution characteristics in shaping propagation patterns, particularly in heterogeneous networks where multiphase dynamics emerge. This research reframes threshold heterogeneity as a controllable structural feature within the emotional diffusion system, offering a refined theoretical foundation and practical guidance for managing negative emotions in social networks and improving system resilience during public crises.