论文标题
使用非负张量分解来了解Covid-19的时空主题动力学：一个案例研究
Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study
论文作者
论文摘要
社交媒体平台通过使数十亿人普遍分享他们的思想和活动来促进人类以数据为导向的世界。如果对数据进行了适当的分析，那么大量的数据可以为人们的行为提供有用的见解。比以往任何时候都更加重要的是，在COVID-19的大流行中，了解人们的在线行为，详细讨论了什么主题，在哪里（空间）以及（时间）讨论了这些主题。鉴于庞大的社交媒体数据的复杂性和质量较差，需要一种有效的时空主题检测方法。本文提出了基于张量的社交媒体数据和非负张量分解（NTF）的表示，以确定社交媒体数据中讨论的主题以及时空主题动态。提出了一项关于COVID-19的相关推文的案例研究，以识别和可视化COVID-19
Social media platforms facilitate mankind a data-driven world by enabling billions of people to share their thoughts and activities ubiquitously. This huge collection of data, if analysed properly, can provide useful insights into people's behavior. More than ever, now is a crucial time under the Covid-19 pandemic to understand people's online behaviors detailing what topics are being discussed, and where (space) and when (time) they are discussed. Given the high complexity and poor quality of the huge social media data, an effective spatio-temporal topic detection method is needed. This paper proposes a tensor-based representation of social media data and Non-negative Tensor Factorization (NTF) to identify the topics discussed in social media data along with the spatio-temporal topic dynamics. A case study on Covid-19 related tweets from the Australia Twittersphere is presented to identify and visualize spatio-temporal topic dynamics on Covid-19
