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Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic

arXiv 2106.09572 ↗First posted 25 May 2021cs.CLcited by 0

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Abstract (arXiv)

Currently, the world is in the midst of a severe global pandemic, which has affected all aspects of people's lives. As a result, there is a deluge of COVID-related digital media articles published in the United States, due to the disparate effects of the pandemic. This large volume of information is difficult to consume by the audience in a reasonable amount of time. In this paper, we develop a Natural Language Processing (NLP) pipeline that is capable of automatically distilling various digital articles into manageable pieces of information, while also modelling the progression topics discussed over time in order to aid readers in rapidly gaining holistic perspectives on pressing issues (i.e., the COVID-19 pandemic) from a diverse array of sources. We achieve these goals by first collecting a large corpus of COVID-related articles during the onset of the pandemic. After, we apply unsupervised and semi-supervised learning procedures to summarize articles, then cluster them based on their similarities using the community detection methods. Next, we identify the topic of each cluster of articles using the BART algorithm. Finally, we provide a detailed digital media analysis based on the NLP-pipeline outputs and show how the conversation surrounding COVID-19 evolved over time.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 46 references that are in the map. First posted on arXiv 25 May 2021.

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References · 16

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4 more
  1. 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
  2. 2020Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approachcited by 20
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