Tweet-mapping Method for Tourist Spots Based on Now-Tweets and Spot-photos
Open accessAbstract
Tourism recommender systems suggest suitable tourist spots by matching the characteristics of the tourist spots with those of the user. In this paper, we focus on an essential source of these characteristics—geotagged tweets. To solve the problem of associating geotagged tweets to tourist spots, we propose a mapping method that infers the region of a target spot on the basis of two geotagged items. The first is a geotagged tweet, which demonstrates that the tweeter was indeed at the target spot at the time the tweet was posted. We call this a “now-tweet.” The second item is a geotagged photo of the target spot, which we call a “spot-photo.” We regard these now-tweets and spot-photos as training data, and then determine the region of the tourist spot by inferring the geographical distribution of the training data. Next, we map geotagged tweets from the extracted region to the target spot. To improve the accuracy with which the tourist spot is inferred, we apply a clustering algorithm to the training data. Experimental results indicate that photo-based mapping with sophisticated training data produces the most improved performance over baseline methods. When applied to 4,559,643 geotagged tweets, our method maps them to tourist spots with an average granularity of 144.85 m.
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Most existing methods for analysing social media data focus on predefined specific questions rather than developing a general understanding of tourists' movement, interests and experiences.
“Most of the existing methods for analysing social media data are focused on finding answers to specific questions that are predefined in their studies (e.g. [10; 53]) and not on developing a general understanding of tourists' movement, interests and experiences (e.g. [41; 77]).”
From A Big Data Analytics Method for Tourist Behaviour Analysis (2017) · cites this paper as [53]
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- Authors
- Oku, K., Hattori, F., & Kawagoe, K.
- Affiliations
- Ritsumeikan University (JP)
- Corresponding author
- Oku, Kenta
- ORCID
- Kawagoe, Kyoji
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2015
- Volume
- 60
- Pages
- 1318-1327
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2613160978
- Cited by
- 17
- References
- 28
- Citations by year
- 2026: 1 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 5 · 2016: 2 · 2015: 1
- FWCI
- 4.43
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Funders
- Japan Society for the Promotion of Science (15K12151)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- geo-tagged tweets, tourism recommender systems, location inference
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