Literature DB >> 33408543

A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression.

Tinggui Chen1, Lijuan Peng1, Xiaohua Yin1, Bailu Jing2, Jianjun Yang3, Guodong Cong4, Gongfa Li5.   

Abstract

BACKGROUND AND AIM: At the end of 2019, the outbreak of COVID-19 had a significant impact on China's tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of incentive policies in order to speed up the recovery of the tourism industry. Therefore, analyzing all sorts of social effects after policy implementation is of guiding significance for the government and the scenic areas.
METHODS: Targeted as the social effect with the implementation of tourism promotion policy during the COVID-19 pandemic, this paper briefly analyzes the impact of COVID-19 on the national cultural and tourism industry and selects several representative types of tourism policies, crawls the comment data of Weibo users, analyzes users' perception and emotional preference to the policy, and thus mines the social effect of various policies. Subsequently, by identifying the social effects of various policies as dependent variables, a binary logistic regression model is constructed to obtain the best combination of tourism promotion policies and promote the rapid revitalization of the cultural and tourism industry.
RESULTS: The results show that from the single policy, the social effect of the "safety" policy is the best. From the perspective of combination policies, the simultaneous release of "safety" policies and "economy" policies have the greatest social impact, which can dramatically accelerate the recovery of the cultural and tourism industry. Finally, this paper proposes suggestions for policy formulation to improve the ability of the cultural tourism industry to cope with crisis events.
CONCLUSION: These results explain the perceived effects of the public on the government policies and can be used to judge whether the policies have been released in place. Based on the above results, corresponding suggestions are proposed as follows: 1) the combination of economic policies and security policies can achieve better results; and 2) the role of "opinion leaders" can be played to improve the perceived effect of policies.
© 2020 Chen et al.

Entities:  

Keywords:  COVID-19; binary logistic regression; combination optimization; data mining; online comments; social effects

Year:  2020        PMID: 33408543      PMCID: PMC7781111          DOI: 10.2147/RMHP.S284564

Source DB:  PubMed          Journal:  Risk Manag Healthc Policy        ISSN: 1179-1594


  5 in total

1.  Optimal price regulations in international pharmaceutical markets with generic competition.

Authors:  Difei Geng; Kamal Saggi
Journal:  J Health Econ       Date:  2020-04-06       Impact factor: 3.883

2.  A machine learning model to assess the ecosystem response to water policy measures in the Tagus River Basin (Spain).

Authors:  Carlotta Valerio; Lucia De Stefano; Gonzalo Martínez-Muñoz; Alberto Garrido
Journal:  Sci Total Environ       Date:  2020-08-01       Impact factor: 7.963

Review 3.  Quantitative approaches for the evaluation of implementation research studies.

Authors:  Justin D Smith; Mohamed Hasan
Journal:  Psychiatry Res       Date:  2019-08-17       Impact factor: 3.222

4.  Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic.

Authors:  Tinggui Chen; Lijuan Peng; Xiaohua Yin; Jingtao Rong; Jianjun Yang; Guodong Cong
Journal:  Healthcare (Basel)       Date:  2020-07-07

5.  Modeling Public Opinion Reversal Process with the Considerations of External Intervention Information and Individual Internal Characteristics.

Authors:  Tinggui Chen; Yulong Wang; Jianjun Yang; Guodong Cong
Journal:  Healthcare (Basel)       Date:  2020-06-05
  5 in total
  5 in total

1.  Propagation Model of Panic Buying Under the Sudden Epidemic.

Authors:  Peihua Fu; Bailu Jing; Tinggui Chen; Chonghuan Xu; Jianjun Yang; Guodong Cong
Journal:  Front Public Health       Date:  2021-04-22

2.  Modeling Multidimensional Public Opinion Polarization Process under the Context of Derived Topics.

Authors:  Tinggui Chen; Yulong Wang; Jianjun Yang; Guodong Cong
Journal:  Int J Environ Res Public Health       Date:  2021-01-08       Impact factor: 3.390

3.  Combining Public Opinion Dissemination with Polarization Process Considering Individual Heterogeneity.

Authors:  Tinggui Chen; Jingtao Rong; Jianjun Yang; Guodong Cong; Gongfa Li
Journal:  Healthcare (Basel)       Date:  2021-02-07

4.  Modeling, simulation, and case analysis of COVID-19 over network public opinion formation with individual internal factors and external information characteristics.

Authors:  Tinggui Chen; Lijuan Peng; Jianjun Yang; Guodong Cong
Journal:  Concurr Comput       Date:  2021-01-23       Impact factor: 1.831

5.  The Impact of COVID-19 on Consumers' Psychological Behavior Based on Data Mining for Online User Comments in the Catering Industry in China.

Authors:  Chenyu Zhang; Jiayue Jiang; Hong Jin; Tinggui Chen
Journal:  Int J Environ Res Public Health       Date:  2021-04-15       Impact factor: 3.390

  5 in total

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