New Approaches to Extract Information From Posts on COVID-19 Published on Reddit

Gianluca Bonifazi, Enrico Corradini, D. Ursino, L. Virgili
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引用次数: 6

Abstract

In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.
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从Reddit上发布的关于COVID-19的帖子中提取信息的新方法
在过去的两年里,我们在社交媒体上看到了关于COVID-19的大量辩论和讨论。许多作者分析了Facebook和Twitter上的这些争论,而很少有人考虑过Reddit。本文以该社交网络为研究对象,提出了三种方法从该社交网络上发布的有关COVID-19的帖子中提取信息。第一个对Reddit帖子执行半自动和动态分类。第二种是自动构建虚拟子reddit,每个子reddit都有相同的主题。第三个自动识别具有相同主题的用户虚拟社区。这三种方法代表了过去文献的进步。事实上,后者缺乏关于分类算法的研究,这些算法能够概括Reddit上数千篇关于COVID-19的帖子之间的差异。类似地,它缺乏能够建立具有相同主题的虚拟子reddit或具有共同兴趣的用户虚拟社区的方法。
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