Travel topic analysis: a mutually reinforcing method for geo-tagged photos

Abstract

Sharing personal activities on social networks is very popular nowadays, where the activities include updating status, uploading dining photos, sharing video clips, etc. Finding travel interests hidden in these vast social activities is an interesting but challenging problem. In this work, we attempt to discover travel interests based on the spatial and temporal information of geo-tagged photos. Obviously the visit sequence of a traveler can be approximately captured by her shared photos based on the timestamps and geo-locations. To extract underlying travel topics from abundant visit sequences, we study a novel mixture model to estimate the visiting probability of regions of attractions (ROAs). Such travel topics can be used in different applications, such as advertisements, promotion strategies, and city planning. To enhance the estimation result, we propose a mutual reinforcement framework to improve the quality of ROAs. Finally, we thoroughly evaluate and demonstrate our findings by the photo sharing activities collected from Flickr TM .

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

“…Studies using the above data processing methods have laid the foundation for the application of geo-tagged photo metadata, such that it is possible to extensively apply data of this kind in multiple research fields. (1) In the field of travel recommendation, Kou et al [27] find that the order in which travelers visit specific locations can be determined from the timestamp and location shown in Flickr shared photos; on this basis, these author shave built a mixed model to estimate the probability that certain interesting places would be visited. Moreover, by utilizing numerous geographical labels and images with text annotation, Chen et al [28] established a distributed geographical image retrieval and recommendation system.…”

Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs

“…Wu et al introduced a tourism hotspot network approach to investigate travel patterns from social media data for tourism resources planning [28]. Moreover, Travel topics or sentiments were discovered from online traveler-generated content, such as Flicker's geo-tagged photos [29,30], tweets [31], or multi-source travelogues [32]. Based on various data sources, travelers' profiles and movement patterns were depicted, tourism market segmentation and travel choice were predicted, and finally the tourism was promoted.…”