Modeling tourism using spatial analysis based on social media big data: a review

Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data has been analyzed in many different disciplines. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly...

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Main Authors: Chen Z, Rayner Alfred, Oliver Valentine Eboy
Format: Conference or Workshop Item
Language:en
en
Published: Springer Singapore 2021
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/30008/1/Modeling%20tourism%20using%20spatial%20analysis%20based%20on%20social%20media%20big%20data%2C%20%20a%20review-Abstract.pdf
https://eprints.ums.edu.my/id/eprint/30008/3/Modeling%20tourism%20using%20spatial%20analysis%20based%20on%20social%20media%20big%20data%2C%20a%20review.pdf
https://eprints.ums.edu.my/id/eprint/30008/
https://www.springerprofessional.de/en/modeling-tourism-using-spatial-analysis-based-on-social-media-bi/18968398
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author Chen Z
Rayner Alfred
Oliver Valentine Eboy
author_facet Chen Z
Rayner Alfred
Oliver Valentine Eboy
author_sort Chen Z
building UMS Library
collection Institutional Repository
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
continent Asia
country Malaysia
description Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data has been analyzed in many different disciplines. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on analyzing the appropriate techniques used to handle different types of data for the purpose of social media analytics. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that three types of data that were least used for social media analytics that includes Bluetooth, WIFI and mobile roaming data. In contrast, other types of data have received more attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analysis social media data.
format Conference or Workshop Item
id my.ums.eprints-30008
institution Universiti Malaysia Sabah
language en
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publishDate 2021
publisher Springer Singapore
record_format eprints
spelling my.ums.eprints-300082021-07-23T06:15:24Z https://eprints.ums.edu.my/id/eprint/30008/ Modeling tourism using spatial analysis based on social media big data: a review Chen Z Rayner Alfred Oliver Valentine Eboy HA Statistics HF Commerce Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data has been analyzed in many different disciplines. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on analyzing the appropriate techniques used to handle different types of data for the purpose of social media analytics. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that three types of data that were least used for social media analytics that includes Bluetooth, WIFI and mobile roaming data. In contrast, other types of data have received more attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analysis social media data. Springer Singapore 2021-03-16 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/30008/1/Modeling%20tourism%20using%20spatial%20analysis%20based%20on%20social%20media%20big%20data%2C%20%20a%20review-Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/30008/3/Modeling%20tourism%20using%20spatial%20analysis%20based%20on%20social%20media%20big%20data%2C%20a%20review.pdf Chen Z and Rayner Alfred and Oliver Valentine Eboy (2021) Modeling tourism using spatial analysis based on social media big data: a review. In: International Conference on Computational Science and Technology, ICCST 2020, 29 - 30 August 2020, Pattaya, Thailand. https://www.springerprofessional.de/en/modeling-tourism-using-spatial-analysis-based-on-social-media-bi/18968398
spellingShingle HA Statistics
HF Commerce
Chen Z
Rayner Alfred
Oliver Valentine Eboy
Modeling tourism using spatial analysis based on social media big data: a review
title Modeling tourism using spatial analysis based on social media big data: a review
title_full Modeling tourism using spatial analysis based on social media big data: a review
title_fullStr Modeling tourism using spatial analysis based on social media big data: a review
title_full_unstemmed Modeling tourism using spatial analysis based on social media big data: a review
title_short Modeling tourism using spatial analysis based on social media big data: a review
title_sort modeling tourism using spatial analysis based on social media big data: a review
topic HA Statistics
HF Commerce
url https://eprints.ums.edu.my/id/eprint/30008/1/Modeling%20tourism%20using%20spatial%20analysis%20based%20on%20social%20media%20big%20data%2C%20%20a%20review-Abstract.pdf
https://eprints.ums.edu.my/id/eprint/30008/3/Modeling%20tourism%20using%20spatial%20analysis%20based%20on%20social%20media%20big%20data%2C%20a%20review.pdf
https://eprints.ums.edu.my/id/eprint/30008/
https://www.springerprofessional.de/en/modeling-tourism-using-spatial-analysis-based-on-social-media-bi/18968398
url_provider http://eprints.ums.edu.my/