An Analysis of Users Engagement on Twitter During the COVID-19 Pandemic: Topical Trends and Sentiments

Sultan Alshamrani, Ahmed Abusnaina, Mohammed Abuhamad, Anho Lee, Dae Hun Nyang, David Mohaisen

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

1 Scopus citations

Abstract

The outbreak of COVID-19 pandemic raised health and economic concerns. With social distancing along with other measures that are enforced in an attempt to limit the spread of the virus, our life has dramatically changed. During this period, the web and social media platforms have become the main medium for communication, expression, and entertainment. Such platforms are a rich source of information, enabling researchers to better understand how the pandemic affected the users’ everyday life, including interaction with and perception of different topics. In this study, we focus on understanding the shift in the behavior of Twitter users, a major social media platform used by millions daily to share thoughts and discussions. In particular, we collected 26 million tweets for a period of seven months, three months before the pandemic outbreak, and four months after. Using topic modeling and state-of-the-art deep learning techniques, the trending topics within the tweets on monthly-bases, including their sentiment and user’s perception, were analyzed. This study highlights the change of the public behavior and concerns during the pandemic. Users expressed their concerns on health services, with an increase of 59.24% in engagement, and economical effects of the pandemic (34.43% increase). Topics such as online shopping have had a remarkable increase in popularity, perhaps due to the social distancing, while crime and sports topics witnessed a decrease. Overall, various topics related to COVID-19 have witnessed an improved sentiment, alluding to users adoption to the pandemic and associated topics of the public discourse.

Original languageEnglish
Title of host publicationComputational Data and Social Networks - 9th International Conference, CSoNet 2020, Proceedings
EditorsSriram Chellappan, Kim-Kwang Raymond Choo, NhatHai Phan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages73-86
Number of pages14
ISBN (Print)9783030660451
DOIs
StatePublished - 2020
Event9th International Conference on Computational Data and Social Networks, CSoNet 2020 - Dallas, United States
Duration: 11 Dec 202013 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12575 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Computational Data and Social Networks, CSoNet 2020
Country/TerritoryUnited States
CityDallas
Period11/12/2013/12/20

Keywords

  • COVID-19
  • Coronavirus
  • NLP
  • Sentiment analysis
  • Topic modeling

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