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Bi-layer voter model: modeling intolerant/tolerant positions and bots in opinion dynamics
The European Physical Journal Special Topics ( IF 2.8 ) Pub Date : 2021-06-06 , DOI: 10.1140/epjs/s11734-021-00151-8
Didier A. Vega-Oliveros , Helder L. C. Grande , Flavio Iannelli , Federico Vazquez

The diffusion of opinions in social networks is a relevant process for adopting positions and attracting potential voters in political campaigns. Opinion polarization, bias, targeted diffusion, and the radicalization of postures are key elements for understanding the voting dynamics’ challenges. In particular, social bots are currently a new element that can have a pronounced effect on the formation of opinions during electoral processes by, for instance, creating fake accounts in social networks to manipulate elections. Here, we propose a voter model incorporating bots and radical or intolerant individuals in the decision-making process. The dynamics of the system occur in a multiplex network of interacting agents composed of two layers, one for the dynamics of opinions where agents choose between two possible alternatives, and the other for the tolerance dynamics, in which agents adopt one of the two tolerance levels. The tolerance accounts 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 an initial stage that scales as \(\tau ^+ \sim \gamma ^{-1} \ln N\), who then reach an opinion consensus during the second stage in a time that scales as \(\tau \sim N\), where N is the number of agents. Therefore, very intolerant agents (\(\gamma \ll 1\)) could considerably slow down dynamics towards the final consensus 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 time that scales as \(\tau _B^- \sim \gamma ^{-1}\) for \(\gamma \ll \sigma _{{\mathbb {B}}}^-\) and \(\tau _B^- \sim 1/\sigma _{{\mathbb {B}}}^-\) for \(\sigma _{{\mathbb {B}}}^- \ll \gamma \).



中文翻译:

双层选民模型:在意见动态中对不容忍/容忍立场和机器人进行建模

意见在社交网络中的传播是在政治竞选中采用立场和吸引潜在选民的相关过程。意见两极分化、偏见、有针对性的传播和姿势的激进化是理解投票动态挑战的关键因素。特别是,社交机器人目前是一种新元素,它可以对选举过程中的意见形成产生显着影响,例如,通过在社交网络中创建虚假账户来操纵选举。在这里,我们提出了一种在决策过程中包含机器人和激进或不宽容的个人的选民模型。系统的动态发生在由两层组成的交互代理的多重网络中,一层用于意见动态,代理在两种可能的选择之间进行选择,另一个用于容忍动态,其中代理采用两个容忍级别之一。容忍度解释了在交互中改变意见的可能性,容忍(不容忍)代理以 1.0 的概率改变意见(\(\gamma \le 1\) )。我们发现,在初始阶段,不容忍导致容忍代理达成共识,其规模为\(\tau ^+ \sim \gamma ^{-1} \ln N\),然后他们在第二阶段达成意见共识缩放为\(\tau \sim N\) 的时间,其中N是代理的数量。因此,非常不宽容的代理(\(\gamma \ll 1\))可能会大大减慢朝向最终共识状态的动态。我们还发现包含一个分数\(\sigma _{{\mathbb {B}}}^-\)bots 打破了两种意见之间的对称性,促使系统对不宽容的代理与 bots 的意见达成共识。因此,机器人最终将他们的意见强加给整个人群,时间为\(\tau _B^- \sim \gamma ^{-1}\) for \(\gamma \ll \sigma _{{\mathbb {B}}}^-\)\(\tau _B^- \sim 1/\sigma _{{\mathbb {B}}}^-\)\(\sigma _{{\mathbb {B} }}^- \ll \gamma \)

更新日期:2021-06-07
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