How pandemic spread in news: Text analysis using topic model

Minghao Wang, Paolo Mengoni

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

COVID-19 pandemic has made tremendous impact on the whole world, both the real world and the media atmosphere. Our research conducted a text analysis using LDA topic model. We first scraped 1127 articles and 5563 comments on SCMP covering COVID-19 from Jan 20 to May 19, then we trained the LDA model and tuned parameters based on the Cv coherence as the model evaluation method. With the optimal model, dominant topics, representative documents of each topic and the inconsistency between articles and comments are analyzed. Some factors of the inconsistency are discussed at last.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
EditorsJing He, Hemant Purohit, Guangyan Huang, Xiaoying Gao, Ke Deng
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages764-770
Number of pages7
ISBN (Electronic)9781665419246
ISBN (Print)9781665430173
DOIs
Publication statusPublished - Dec 2020
Event2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 (Virtual Conference) - Virtual, Online, Melbourne, Australia
Duration: 14 Dec 202017 Dec 2020

Publication series

NameProceedings - IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT

Conference

Conference2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 (Virtual Conference)
Country/TerritoryAustralia
CityMelbourne
Period14/12/2017/12/20

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • COVID-19
  • Text analysis
  • Topic modeling

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