Tourism Network Attention Variation of Chinese Cities under the COVID-19 Pandemic

Abstract

At the end of 2019, the COVID-19 pandemic broke out globally and had a tremendous impact on tourism development in countries around the world. The rapid shift of tourism from “over-tourism” to “under-tourism”, threatening the future of the global economy and society, has generated considerable interest from academia and the policy community, but the impact of COVID-19 on tourism variation remains untested by empirical evidence. Based on the daily Baidu Index of 247 prefecture-level cities in China from 2018 to 2021, this study assessed the treatment effect of COVID-19 on tourism and analyzed its dynamic characteristics using the regression-discontinuity-design (RDD) method combined with tourism network attention (TNA) data. The results show that after the outbreak of the COVID-19 pandemic, the level value of TNA dropped significantly by 2.12 (p < 0.10), and the difference value of TNA (TNA_diff) dropped significantly by 10.77 (p < 0.01), indicating that COVID-19 has a negative causal effect on tourism development, and its impact is more pronounced in major tourist source cities, with a coefficient of −14.91 (p < 0.01) corresponding to −4.57 (p < 0.01) for non-major tourist source cities when the dependent variable TNA_diff. The identification of dynamic effects further confirms that the negative impact of the pandemic on tourism network attention is fluctuating and persistent during the study period, with the two major “golden weeks” and peak season being the most severe. Compared to 2020, the TNA has generally shown an upward trend since 2021, indicating signs of a rebound in the vitality of resident tourism, which is conducive to the healthy development of the tourism market.

The network characteristics of classic red tourist attractions in Shaanxi province, China

“…For example, some scholars have used the network attention of websites such as Qunar, Ctrip, and Dianping as data to study the coupling between network attention and scenic attraction [40]. At the same time, scholars have also paid attention to the influencing factors of tourism online attention [41]. Research has found that important factors influencing online attention include economic level, population size, distance, climate, and transportation [42].…”

A study on the spatial differences between the tourism network attention and tourism flow in Shanghai, China

“…Network attention is a statistical standard based on social media and search engine data to assess the popularity of specific keywords, reflecting the degree of user attention towards specific objects [ 1 ]. The application of network attention in the tourism sector also reflects tourists' attitudes towards related tourism elements and phenomena [ 2 ], profoundly influencing tourists' behavior [ 3 , 4 ]. It has been widely used to predict tourism demand and assess the development potential for tourism destinations [ [5] , [6] , [7] , [8] ].…”

Correction: Hou, X. Tourism Network Attention Variation of Chinese Cities under the COVID-19 Pandemic. Sustainability 2022, 14, 5131

“…The authors would like to make the following corrections to the published paper [1]. The changes are as follows:…”