Tourism demand forecasting with online news data mining

Eunhye Park; Jinah Park; Mingming Hu

doi:10.1016/j.annals.2021.103273

Tourism demand forecasting with online news data mining

Eunhye Park, Jinah Park, Mingming Hu

Research output: Journal article publication › Journal article › Academic research › peer-review

55 Citations (Scopus)

Abstract

This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.

Original language	English
Article number	103273
Journal	Annals of Tourism Research
Volume	90
DOIs	https://doi.org/10.1016/j.annals.2021.103273
Publication status	Published - Sept 2021

Keywords

Hong Kong
News discourse
Topic modeling
Tourism demand forecasting

ASJC Scopus subject areas

Development
Tourism, Leisure and Hospitality Management

More information

10.1016/j.annals.2021.103273

http://hdl.handle.net/10397/93089

Cite this

@article{56883cfc2542466fb2829e6ae4df9300,

title = "Tourism demand forecasting with online news data mining",

abstract = "This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.",

keywords = "Hong Kong, News discourse, Topic modeling, Tourism demand forecasting",

author = "Eunhye Park and Jinah Park and Mingming Hu",

note = "Funding Information: This work was supported by the National Natural Science Foundation of China ( 71761001 , 71761002 ), the Guangxi Key Research and Development Plan ( Guike-AB20297040 ), and Hong Kong Scholars Program . Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",

year = "2021",

month = sep,

doi = "10.1016/j.annals.2021.103273",

language = "English",

volume = "90",

journal = "Annals of Tourism Research",

issn = "0160-7383",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Tourism demand forecasting with online news data mining

AU - Park, Eunhye

AU - Park, Jinah

AU - Hu, Mingming

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China ( 71761001 , 71761002 ), the Guangxi Key Research and Development Plan ( Guike-AB20297040 ), and Hong Kong Scholars Program . Publisher Copyright: © 2021 Elsevier Ltd

PY - 2021/9

Y1 - 2021/9

N2 - This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.

AB - This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.

KW - Hong Kong

KW - News discourse

KW - Topic modeling

KW - Tourism demand forecasting

UR - http://www.scopus.com/inward/record.url?scp=85110362907&partnerID=8YFLogxK

U2 - 10.1016/j.annals.2021.103273

DO - 10.1016/j.annals.2021.103273

M3 - Journal article

AN - SCOPUS:85110362907

SN - 0160-7383

VL - 90

JO - Annals of Tourism Research

JF - Annals of Tourism Research

M1 - 103273

ER -

Tourism demand forecasting with online news data mining

Abstract

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this