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COVID-19 and mobility in tourism cities: A statistical change-point detection approach

COVID-19 and mobility in tourism cities: A statistical change-point detection approach

Yang, Mu, Han, Chunjia, Cui, Yongmei and Zhao, Yuanjun (2021) COVID-19 and mobility in tourism cities: A statistical change-point detection approach. Journal of Hospitality and Tourism Management, 47. pp. 256-261. ISSN 1447-6770 (Print), 1839-5260 (Online) (doi:https://doi.org/10.1016/j.jhtm.2021.03.014)

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Abstract

This study analyses the agenda setting on social media in the COVID-19 pandemic by exploiting one of the disruptive technologies, big data analytics. Our purpose is to examine whether the agenda of news organisations matches the public agenda on social media in crisis situations, and to explore the feasibility and efficacy of applying big data analytics on social media data. To this end, we used an unsupervised machine learning approach, structural topic modelling and analysed 129,965 tweets posted by UK news media and citizens during April 2, and 8, 2020. Our study reveals a wide diversity of topics in the tweets generated by both groups and finds only a small number of topics are similar, indicating different agendas set in the pandemic. Moreover, we show that citizen tweets focused more on expressing feelings and sharing personal activities while news media tweets talked more about facts and analysis on COVID-19. In addition, our results find that citizens responded more significantly to breaking news. The findings of the study contribute to the agenda setting literature and offer valuable practical implications.

Item Type: Article
Additional Information: © 2021 The Authors.
Uncontrolled Keywords: Mobility; Tourism cities; COVID-19; Change-point detection
Subjects: G Geography. Anthropology. Recreation > GV Recreation Leisure
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified: 11 Jun 2021 09:35
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/31857

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