Mining Links Between Travel Motivations and Mental Representation of Destinations from Twitter Postings

Daniel Hardt; Fumiko  Kano Glückstad

doi:10.15444/GMC2020.08.01.02

Mining Links Between Travel Motivations and Mental Representation of Destinations from Twitter Postings

Daniel Hardt, Fumiko Kano Glückstad

Department of Management, Society and Communication

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Abstract

This paper describes Natural Language Processing techniques to extract patterns of destination image formation from Social Media postings. In a pilot study, we show how Twitter postings can be mined to uncover travel motivations and values for various travel destinations; we also explore the mental representations that underly these values and motivations. While this pilot study is subject to limitations, the results demonstrate that Twitter data is a compelling source of information about travellers and destinations.

Originalsprog	Engelsk
Titel	2020 Global Marketing Conference at Seoul Proceedings
Redaktører	Jeonghye Choi, Yeolim Yoon
Antal sider	5
Udgivelsessted	Changwon
Forlag	Global Alliance of Marketing & Management Associations
Publikationsdato	2020
Sider	1025-1029
DOI	https://doi.org/10.15444/GMC2020.08.01.02
Status	Udgivet - 2020
Begivenhed	2020 Global Marketing Conference: Bridging Asia and the World: New Marketing and Management in Digitally Connected World - Virtual Conference, Seoul, WWW Varighed: 5 nov. 2020 → 8 nov. 2020

Konference

Konference	2020 Global Marketing Conference
Lokation	Virtual Conference
Land/Område	WWW
By	Seoul
Periode	05/11/2020 → 08/11/2020

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10.15444/GMC2020.08.01.02Licens: Ikke-specificeret

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Citationsformater

@inproceedings{814c37892cc848149bdb5b8c42f84c46,

title = "Mining Links Between Travel Motivations and Mental Representation of Destinations from Twitter Postings",

abstract = "This paper describes Natural Language Processing techniques to extract patterns of destination image formation from Social Media postings. In a pilot study, we show how Twitter postings can be mined to uncover travel motivations and values for various travel destinations; we also explore the mental representations that underly these values and motivations. While this pilot study is subject to limitations, the results demonstrate that Twitter data is a compelling source of information about travellers and destinations.",

author = "Daniel Hardt and {Kano Gl{\"u}ckstad}, Fumiko",

year = "2020",

doi = "10.15444/GMC2020.08.01.02",

language = "English",

pages = "1025--1029",

editor = "Jeonghye Choi and Yeolim Yoon",

booktitle = "2020 Global Marketing Conference at Seoul Proceedings",

publisher = "Global Alliance of Marketing & Management Associations",

address = "Korea, Republic of",

note = "2020 Global Marketing Conference ; Conference date: 05-11-2020 Through 08-11-2020",

}

Hardt, D & Kano Glückstad, F 2020, Mining Links Between Travel Motivations and Mental Representation of Destinations from Twitter Postings. i J Choi & Y Yoon (red), 2020 Global Marketing Conference at Seoul Proceedings. Global Alliance of Marketing & Management Associations, Changwon, s. 1025-1029, 2020 Global Marketing Conference, Seoul, WWW, 05/11/2020. https://doi.org/10.15444/GMC2020.08.01.02

Mining Links Between Travel Motivations and Mental Representation of Destinations from Twitter Postings. / Hardt, Daniel ; Kano Glückstad, Fumiko .
2020 Global Marketing Conference at Seoul Proceedings. red. / Jeonghye Choi; Yeolim Yoon. Changwon: Global Alliance of Marketing & Management Associations, 2020. s. 1025-1029.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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AU - Hardt, Daniel

AU - Kano Glückstad, Fumiko

PY - 2020

Y1 - 2020

N2 - This paper describes Natural Language Processing techniques to extract patterns of destination image formation from Social Media postings. In a pilot study, we show how Twitter postings can be mined to uncover travel motivations and values for various travel destinations; we also explore the mental representations that underly these values and motivations. While this pilot study is subject to limitations, the results demonstrate that Twitter data is a compelling source of information about travellers and destinations.

AB - This paper describes Natural Language Processing techniques to extract patterns of destination image formation from Social Media postings. In a pilot study, we show how Twitter postings can be mined to uncover travel motivations and values for various travel destinations; we also explore the mental representations that underly these values and motivations. While this pilot study is subject to limitations, the results demonstrate that Twitter data is a compelling source of information about travellers and destinations.

U2 - 10.15444/GMC2020.08.01.02

DO - 10.15444/GMC2020.08.01.02

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BT - 2020 Global Marketing Conference at Seoul Proceedings

A2 - Choi, Jeonghye

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CY - Changwon

T2 - 2020 Global Marketing Conference

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