# Mass media and its impact on opinion dynamics of the nonlinear $q$-voter model Source: [Mass media and its impact on opinion dynamics of the nonlinear $q$-voter model]() Collection index: [llms.txt]() Data updated: 2025-10-25T05:28:54.105Z Authors: Roni Muslim, Rinto Anugraha NQZ, Muhammad Ardhi Khalif Published: 2023-05-10T11:11:23.000Z ## Abstract With the success of general conceptual frameworks of statistical physics, many scholars have tried to apply these concepts to other interdisciplinary fields, such as socio-politics, economics, biology, medicine, and many more. In this work, we study the effect of mass media on opinion evolution based on the nonlinear $q$-voter by means with probability $p$ a voter adopts the mass media opinion whenever a $q$-sized agent in the population is not in unanimous agreement. We perform analytical and numerical calculations for some quantities of macroscopic parameters of the model such as order parameter (representing an average of public opinion), consensus (relaxation) time, and exit probability, and obtain the agreement results. We find the power-law relations for some quantities of the model. (1) The probability threshold $p\_t$, i.e a probability that makes the system reaches a homogeneous state, follows the power-law relation $p\_t \\sim q^{\\gamma}$ with the $q$-sized agent, where $\\gamma = -1.00 \\pm 0.01$ is the best fitting parameter. The probability threshold $p\_t$ also eliminates the {coexistence two ordered states} of the model. (2) The relaxation time (the time needed by the system to reach consensus) $\\tau$ with the population size $N$ is obtained in the form of $\\tau \\sim N^{\\delta}$, where $\\delta$ depends on the probability $p$ and $q$-sized agent. We also approximate the {separator} point {$r\_s$} and the system's scaling parameters by employing the standard finite-size scaling relation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Panic Classification on Hurricane Sandy dataset (test)](): Panic Classification; Hurricane Sandy dataset (test); Accuracy: 60.57. - [Macro-level panic propagation prediction on Hurricane Sandy](): Macro-level panic propagation prediction; Hurricane Sandy; Peak Count: 90. ## Original sources and implementation [Original paper]() [PDF]()