ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
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ReCOVery：用于 COVID-19 新闻可信度研究的多模式存储库
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
R. Zafarani
中科院分区：
文献类型：
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作者：
Xinyi Zhou;Apurva Mulay;Emilio Ferrara;R. Zafarani
世界卫生组织 (WHO) 于 2019 年 12 月首次在中国武汉发现了新冠肺炎 (COVID-19) 疫情，并于 1 月宣布为全球紧急情况，并于 2020 年 3 月宣布为大流行病。伴随着这次疫情，我们还面临着假新闻、阴谋论等低可信度信息的“信息流行病”。在这项工作中，我们提出了 ReCOVery，这是一个设计和构建的存储库，旨在促进对抗有关 COVID-19 的此类信息的研究。我们首先广泛搜索和调查约 2,000 家新闻出版商，从中确定 60 家具有极高或极低的可信度。通过继承发布媒体的可信度，存储库中收集了 2020 年 1 月至 5 月发表的总共 2,029 篇有关冠状病毒的新闻文章，以及揭示这些新闻文章如何在 Twitter 社交网络上传播的 140,820 条推文。该存储库提供有关冠状病毒的新闻文章的多模态信息，包括文本、视觉、时间和网络信息。获得新闻可信度的方式允许在数据集可扩展性和标签准确性之间进行权衡。进行了大量的实验来呈现数据统计和分布，并提供预测新闻可信度的基线性能，以便可以比较未来的方法。我们的存储库位于 http://coronavirus-fakenews.com。
First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http://coronavirus-fakenews.com.
CoAID：COVID-19 医疗保健错误信息数据集
DOI：
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发表时间：
2020
期刊：
arXiv
影响因子：
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作者：
Cui, Limeng;Lee, Dongwon
通讯作者：
Lee, Dongwon