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The diffusion of opinions in Social Networks is a relevant process foradopting positions and attracting potential voters in political campaigns.Opinion polarization, bias, targeted diffusion, and the radicalization ofpostures are key elements for understanding voting dynamics. In particular,social bots are a new element that can have a pronounced effect on theformation of opinions during elections by, for instance, creating fake accountsin social networks to manipulate elections. Here we propose a voter modelincorporating bots and radical or intolerant individuals in the decision-makingprocess. The dynamics of the system occur in a multiplex network of interactingagents composed of two layers, one for the dynamics of opinions where agentschoose between two possible alternatives, and the other for the tolerancedynamics, in which agents adopt one of two tolerance levels. The toleranceaccounts for the likelihood to change opinion in an interaction, with tolerant(intolerant) agents switching opinion with probability $1.0$ ($\gamma \le 1$).We find that intolerance leads to a consensus of tolerant agents during aninitial stage that scales as $\tau^+ \sim \gamma^{-1} \ln N$, who then reach anopinion consensus during the second stage in a time that scales as $\tau \simN$, where $N$ is the number of agents. Therefore, very intolerant agents($\gamma \ll 1$) could considerably slow down dynamics towards the finalconsensus state. We also find that the inclusion of a fraction$\sigma_{\mathbb{B}}^-$ of bots breaks the symmetry between both opinions,driving the system to a consensus of intolerant agents with the bots' opinion.Thus, bots eventually impose their opinion to the entire population, in a timethat scales as $\tau_B^- \sim \gamma^{-1}$ for $\gamma \ll\sigma_{\mathbb{B}}^-$ and $\tau_B^- \sim 1/\sigma_{\mathbb{B}}^-$ for$\sigma_{\mathbb{B}}^- \ll \gamma$.

Bi-layer voter model: Modeling intolerant/tolerant positions and bots in opinion dynamics