论文标题
使用复杂的网络和文本挖掘表征Covid-19期间Twitter相互作用的表征
Characterizing Twitter Interaction during COVID-19 pandemic using Complex Networks and Text Mining
论文作者
论文摘要
Covid-19的爆发始于许多月前，报道起源于中国武汉市场。迅速地，该病毒被传播到其他国家，因为获得国际旅行的机会是负担得起的，而且许多国家的飞行时间距离距离，除了边界不断流动。另一方面，互联网用户有使用社交网络和问题，问题，关于CovdID-19的想法共享内容的习惯，这不是例外。因此，有可能分析一个城市，国家的社交网络互动，以了解该全球问题产生的影响。南美是一个具有发展中国家的地区，面临与政治，经济，公共卫生和其他地区有关的挑战。因此，本文的范围是分析南美国家的Twitter上的相互作用，并使用复杂的网络表示和文本挖掘来通过用户来表征数据流。初步实验引入了模式存在的概念，类似于复杂的系统。此外，学位分布确认了拥有系统和可视化邻接矩阵的想法，显示了用户群体发布和在此期间共同交互的存在，有可能识别机器人不断发送帖子。
The outbreak of covid-19 started many months ago, the reported origin was in Wuhan Market, China. Fastly, this virus was propagated to other countries because the access to international travels is affordable and many countries have a distance of some flight hours, besides borders were a constant flow of people. By the other hand, Internet users have the habits of sharing content using Social Networks and issues, problems, thoughts about Covdid-19 were not an exception. Therefore, it is possible to analyze Social Network interaction from one city, country to understand the impact generated by this global issue. South America is one region with developing countries with challenges to face related to Politics, Economy, Public Health and other. Therefore, the scope of this paper is to analyze the interaction on Twitter of South American countries and characterize the flow of data through the users using Complex Network representation and Text Mining. The preliminary experiments introduces the idea of existence of patterns, similar to Complex Systems. Besides, the degree distribution confirm the idea of having a System and visualization of Adjacency Matrices show the presence of users' group publishing and interacting together during the time, there is a possibility of identification of robots sending posts constantly.
