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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Singapore
2021
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| 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 |
| _version_ | 1825714280519958528 |
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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 |
| collection | UMS |
| 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. |
| first_indexed | 2024-03-06T03:09:27Z |
| format | Conference or Workshop Item |
| id | ums.eprints-30008 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:09:27Z |
| publishDate | 2021 |
| publisher | Springer Singapore |
| record_format | dspace |
| spelling | 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 |
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