论文标题
一种基于多公开的方法，用于量化社交网络的两极分化
A Multi-Opinion Based Method for Quantifying Polarization on Social Networks
论文作者
论文摘要
社交媒体平台已成为政治和社会互动的枢纽，分析观点的两极分化一直引起人们的关注。在这项研究中，我们提出了一项衡量社交网络两极分化的措施。与最先进的方法不同，所提出的指标不假定两次案例，并且适用于多种观点。我们在不同的网络上测试了我们的度量，并具有多开放的情况和不同程度的极化。从拟议的度量标准获得的分数与基于二进制意见的基准网络上的最新方法相媲美。该技术还区分了在多欧元场景中具有不同极化水平的网络。我们还量化了从Twitter获得的转发网络中的极化，以在治疗Covid-19中使用羟氯喹或氯喹等药物。我们的指标表明用户之间有高度两极分化的意见。这些发现表明，用户对使用羟氯喹和氯喹治疗COVID-19患者的好处的不确定性。
Social media platforms have emerged as a hub for political and social interactions, and analyzing the polarization of opinions has been gaining attention. In this study, we have proposed a measure to quantify polarization on social networks. The proposed metric, unlike state-of-the-art methods, does not assume a two-opinion case and applies to multiple opinions. We tested our metric on different networks with a multi-opinion scenario and varying degrees of polarization. The scores obtained from the proposed metric were comparable to state-of-the-art methods on binary opinion-based benchmark networks. The technique also differentiated among networks with different levels of polarization in a multi-opinion scenario. We also quantified polarization in a retweet network obtained from Twitter regarding the usage of drugs like hydroxychloroquine or chloroquine in treating COVID-19. Our metric indicated a high level of polarized opinions among the users. These findings suggest uncertainty among users in the benefits of using hydroxychloroquine and chloroquine drugs to treat COVID-19 patients.
