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首页> 外文期刊>Computers, Materials & Continua >What is Discussed about COVID-19: A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling
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What is Discussed about COVID-19: A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

机译：关于Covid-19的讨论：一种多模态框架，用于分析来自新浪微博的微博没有人类标签

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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.
机译：从2019年底开始，新的冠状病毒病（Covid-19）已成为全球危机。随着在线社交媒体的发展，人们更愿意在线表达他们的意见并讨论最新消息。我们目睹了在线社交媒体的积极影响，这有助于公民和政府及时跟踪这种大流行的发展。有必要将人工智能（AI）技术应用于在线社交媒体，并自动发现和跟踪在线发布的公众意见。在本文中，我们采取了新浪微博，是中国最广泛使用的在线社交媒体，进行分析和实验。我们从2020 / 1/1到2020 / 3/31收集关于Covid-19的多模态微博，其中包含Web爬网程序，包括用户发布的文本和图像。为了有效地发现关于Covid-19没有人类标签的讨论，我们提出了一个统一的多模态框架，包括无监督的短文本主题模型来发现和跟踪突发主题，以及用于学习图像的自我监督模型功能使我们可以检索关于Covid-19的相关图像。实验结果表明了拟议模型的有效性和优势，并显示了对分析和跟踪Covid-19的公众意见的相当大的应用前景。

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  • 来源
    《Computers, Materials & Continua》 |2020年第3期|1453-1471|共19页
  • 作者

    Hengyang Lu; Yutong Lou; Bin Jin; Ming Xu;

  • 作者单位

    School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China;

    National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210023 China;

    National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210023 China;

    National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210023 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    COVID-19; public opinion; microblog; topic model; self-supervised learning;

    机译：新冠肺炎;舆论;微博;主题模型;自我监督的学习;

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