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

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › 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.

Original language	English
Title of host publication	2020 Global Marketing Conference at Seoul Proceedings
Editors	Jeonghye Choi, Yeolim Yoon
Number of pages	5
Place of Publication	Changwon
Publisher	Global Alliance of Marketing & Management Associations
Publication date	2020
Pages	1025-1029
DOIs	https://doi.org/10.15444/GMC2020.08.01.02
Publication status	Published - 2020
Event	2020 Global Marketing Conference: Bridging Asia and the World: New Marketing and Management in Digitally Connected World - Virtual Conference, Seoul, WWW Duration: 5 Nov 2020 → 8 Nov 2020

Conference

Conference	2020 Global Marketing Conference
Location	Virtual Conference
Country/Territory	WWW
City	Seoul
Period	05/11/2020 → 08/11/2020

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10.15444/GMC2020.08.01.02Licence: Unspecified

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Cite this

@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 = "null ; 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. in J Choi & Y Yoon (eds), 2020 Global Marketing Conference at Seoul Proceedings. Global Alliance of Marketing & Management Associations, Changwon, pp. 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. ed. / Jeonghye Choi; Yeolim Yoon. Changwon : Global Alliance of Marketing & Management Associations, 2020. p. 1025-1029.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AU - Hardt, Daniel

AU - Kano Glückstad, Fumiko

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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.

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