Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

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dc.contributor.authorOrdun, Catherine
dc.contributor.authorPurushotham, Sanjay
dc.contributor.authorRaff, Edward
dc.date.accessioned2020-10-21T18:39:45Z
dc.date.available2020-10-21T18:39:45Z
dc.date.issued2020-05-06
dc.description.abstractThis 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.en_US
dc.description.urihttps://arxiv.org/abs/2005.03082en_US
dc.format.extent19 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m24rdv-a0gg
dc.identifier.citationOrdun, Catherine; Purushotham, Sanjay; Raff, Edward; Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs; Social and Information Networks (2020); https://arxiv.org/abs/2005.03082en_US
dc.identifier.urihttp://hdl.handle.net/11603/19949
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectcovid-19en_US
dc.subjecttopic modelingen_US
dc.subjectUMAPen_US
dc.subjectDiGraphsen_US
dc.titleExploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphsen_US
dc.typeTexten_US

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