@Article{cmc.2020.011270,
AUTHOR = {Hengyang Lu, Yutong Lou, Bin Jin, Ming Xu},
TITLE = {What is Discussed about COVID-19: A Multi-Modal Framework  for Analyzing Microblogs from Sina Weibo without Human Labeling},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1453--1471},
URL = {http://www.techscience.com/cmc/v64n3/39439},
ISSN = {1546-2226},
ABSTRACT = {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 selfsupervised 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.},
DOI = {10.32604/cmc.2020.011270}
}