The rise of social networks as the primary means of communication in almost every country in the world has simultaneously triggered an increase in the amount of fake news circulating online. This fact became particularly evident during the 2016 U.S. political elections and even more so with the advent of the COVID-19 pandemic. Several research studies have shown how the effects of fake news dissemination can be mitigated by promoting greater competence through lifelong learning and discussion communities, and generally rigorous training in the scientific method and broad interdisciplinary education. The urgent need for models that can describe the growing infodemic of fake news has been highlighted by the current pandemic. The resulting slowdown in vaccination campaigns due to misinformation and generally the inability of individuals to discern the reliability of information is posing enormous risks to the governments of many countries. In this research using the tools of kinetic theory we describe the interaction between fake news spreading and competence of individuals through multi-population models in which fake news spreads analogously to an infectious disease with different impact depending on the level of competence of individuals. The level of competence, in particular, is subject to an evolutionary dynamic due to both social interactions between agents and external learning dynamics. The results show how the model is able to correctly describe the dynamics of diffusion of fake news and the important role of competence in their containment.
翻译:在2016年美国政治选举期间,这个事实尤其明显,随着COVID-19大流行的到来,这个事实更加明显。一些研究显示,如何通过提高终身学习和讨论社区的能力,通过在科学方法和广泛的跨学科教育方面普遍进行严格的培训,减轻虚假新闻传播的影响,从而通过提高科学方法和广泛跨学科教育的能力,普遍提高社会网络作为世界几乎每一个国家的主要通信手段的兴起,这同时引发了假新闻在网上传播的数量的增加。目前这一大流行病突出表明,迫切需要能够描述假新闻日益流行的模式。由于错误信息以及个人一般无法识别信息可靠性,因此疫苗接种运动速度放慢,对许多国家政府构成了巨大风险。在这项研究中,我们利用动能理论工具描述了假新闻传播与个人能力之间的相互作用。在多人口模型中,假新闻传播类似于对个人能力有不同影响的传染病。能力水平因代理人和外部学习动态之间的社会互动而受进化动态影响。结果显示,模型能够正确描述其重要信息传播的动态。