A graph-based approach to detecting tourist movement patterns using social media data
Bibliographic Data
| ID | 12751331 |
|---|---|
| Authors | Fei Hu (0000-0001-7827-9548, George Mason University), Zhenlong Li (0000-0002-8938-5466, University of South Carolina, corresponding author), Chaowei Yang (0000-0001-7768-4066, George Mason University), Yongyao Jiang (0000-0002-4591-483X, George Mason University) |
| Year | 2018 |
| Volume | 46 |
| Issue | 4 |
| Pages | 368-382 |
| Publication date | 2018-09-17 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartography and Geographic Information Science (JOURNAL) |
| Journal identifiers | ISSN: 1523-0406 • E-ISSN: 1545-0465 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/15230406.2018.1496036 |
| OpenAlex | W2890992808 |
| Language | EN |
| Citations received | 17 |
| References cited | 45 |
Understanding the characteristics of tourist movement is essential for tourist behavior studies since the characteristics underpin how the tourist industry management selects strategies for attraction planning to commercial product development. However, conventional tourism research methods are not either scalable or cost-efficient to discover underlying movement patterns due to the massive datasets. With advances in information and communication technology, social media platforms provide big data sets generated by millions of people from different countries, all of which can be harvested cost efficiently. This paper introduces a graph-based method to detect tourist movement patterns from Twitter data. First, collected tweets with geo-tags are cleaned to filter those not published by tourists. Second, a DBSCAN-based clustering method is adapted to construct tourist graphs consisting of the tourist attraction vertices and edges. Third, network analytical methods (e.g. betweenness centrality, Markov clustering algorithm) are applied to detect tourist movement patterns, including popular attractions, centric attractions, and popular tour routes. New York City in the United States is selected to demonstrate the utility of the proposed methodology. The detected tourist movement patterns assist business and government activities whose mission is tour product planning, transportation, and development of both shopping and accommodation centers
Betweenness centrality · Centrality · Cluster analysis · Data mining · Data science · Database · Geography · Product (mathematics · Scalability · Social media · Tourism · World Wide Web · Computer Science · Digital Marketing and Social Media · Diverse Aspects of Tourism Research · Human Mobility and Location-Based Analysis · Mathematics · Artificial Intelligence
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| Unique citing works | 17 |
|---|---|
| Citations per year | 2.83 |
| Citation span | 2020 - 2025 (6) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 17 |