Open Access

ARTICLE

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

by Hengyang Lu, Yutong Lou, Bin Jin, Ming Xu

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.

* Corresponding Author: Hengyang Lu. Email: .

(This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)

Computers, Materials & Continua 2020, 64(3), 1453-1471. https://doi.org/10.32604/cmc.2020.011270

Received 29 April 2020; Accepted 23 May 2020; Issue published 30 June 2020

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.

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Citations