Knowledge Extraction About Covid Variant in Social Media (Twitter) Using Social Network Analysis (SNA) and Latent Dirichlet Allocation (LDA)

Anton Ade Purnama, Arsad Saifunas, Dana Indra Sensuse, And Nadya Safitri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

When the coronavirus disease 2019 (COVID-19) pandemic became a major global health disaster, evidence that SARS-CoV-2 might mutate grew; at this time, many SARS-CoV-2 variants are circulating globally. The information about Covid variants is spread on social media such as Twitter. It may disseminate fake news or incorrect information if the major participants in spreading Covid variations are not users with knowledge of the topic, and it may drive an individual to disregard government advice about social distance and other public health actions. The purposes of the research are to identify key players in the spreading of that information using the Social Network method Analyst (SNA) on the social network Twitter and to know what information and topics are contained in the Covid variant tweets using Latent Dirichlet Allocation (LDA). Hashtag #covidvariant is used to collect Twitter data using Twitter API from November 21st to December 6th, 2021. The Twitter API returned 47,124 tweets to be analyzed. The result of this research found that the key players are the accounts that have knowledge of Covid variants, and 10 big topics shared on Twitter regarding Covid variants. Most key players that are found are India Today, Doctor Soumya, POTUS, and WHO. The top 10 big topics that are shared on Twitter regarding Covid variants are lockdown, travel restriction, India's concern about Covid mutation, a new Covid variant in Africa, vaccine booster for Omicron, wearing a cloth face mask, Omicron case in Maharashtra, Joe Biden's statement about Omicron, Omicron impact to the business, unvaccinated kid case.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
EditorsYessi Jusman, Ahmad Zaki, Cahya Damarjati
PublisherAmerican Institute of Physics Inc.
Edition1
ISBN (Electronic)9780735447912
DOIs
Publication statusPublished - 28 Dec 2023
Event3rd International Conference on Information Technology and Advanced Mechanical and Electrical Engineering, ICITAMEE 2022 - Yogyakarta, Indonesia
Duration: 20 Jul 202221 Jul 2022

Publication series

NameAIP Conference Proceedings
Number1
Volume2865
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Conference on Information Technology and Advanced Mechanical and Electrical Engineering, ICITAMEE 2022
Country/TerritoryIndonesia
CityYogyakarta
Period20/07/2221/07/22

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