使用主题建模、UMAP 和有向图对 Covid-19 推文进行探索性分析
Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs
AI总结：
本文通过模式匹配、LDA 主题建模、UMAP 聚类、转发时间计算和转发级联可视化五种方法分析 Covid-19 推文，揭示关键主题、传播速度与网络行为，并验证了正则表达式等早期描述性方法的有效性。
摘要：
本文展示了五种不同的技术，用于评估 Covid-19 推文的主题独特性、关键术语和特征、信息传播速度以及网络行为。首先，我们使用模式匹配；其次，通过潜在狄利克雷分配（LDA）进行主题建模，生成二十个不同主题，讨论病例传播、医护人员和个人防护装备（PPE）。其中一个与美国病例相关的主题会在白宫冠状病毒特别工作组现场简报会之后立即开始上升，这表明许多 Twitter 用户正在关注政府公告。我们贡献了此前未在 Covid-19 Twitter 文献中报道过的机器学习方法。这包括我们的第三种方法，统一流形逼近与投影（UMAP），该方法识别不同主题的独特聚类行为，以增进我们对语料库中重要主题的理解，并帮助评估所生成主题的质量。第四，我们计算了转发时间，以了解 Covid-19 相关信息在 Twitter 上的传播速度。我们的分析表明，2020 年 3 月样本语料库中 Covid-19 的转发时间中位数为 2.87 小时，比 2013 年 3 月中国社交媒体上关于 H7N9 的转发大约快 50 分钟。最后，我们试图通过可视化用户随时间从快速转发到慢速转发的连接关系来理解转发级联。随着转发时间的增加，连接的密度也随之增加，在我们的样本中，我们发现不同的用户主导着 Covid-19 转发者的注意力。该分析最简单的亮点之一是，诸如正则表达式之类的早期描述性方法能够成功识别出高层次主题，而这些主题在随后的每一项分析中都被一致验证为重要主题。
英文摘要：
This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
