What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling  

作　　者：Hengyang Lu Yutong Lou Bin Jin Ming Xu 

机构地区：[1]School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi,214122,China [2]National Key Laboratory for Novel Software Technology,Department of Computer Science and Technology,Nanjing University,Nanjing,210023,China

出　　处：《Computers, Materials & Continua》2020年第9期1453-1471,共19页计算机、材料和连续体（英文）

基　　金：This paper is supported by the Fundamental Research Funds for the Central Universities[No.JUSRP12021].

摘　　要：Starting from late 2019,the new coronavirus disease(COVID-19)has become a global crisis.With the development of online social media,people prefer to express their opinions and discuss the latest news online.We have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in time.It is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted online.In this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and experiments.We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by users.In order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about COVID-19.Experimental results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

关 键 词：COVID-19 public opinion microblog topic model self-supervised learning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]