Incremental Tag Suggestion for Landmark Image Collections

In recent social media applications, descriptive information is collected through user tagging, such as face recognition, and automatic environment sensing, such as GPS. There are many applications that recognize landmarks using information gathered from GPS data. However, GPS is dependent on the location of the camera, not the landmark. In this research, we propose an automatic landmark tagging scheme using secondary regions to distinguish between similar landmarks. We propose two algorithms: 1) landmark tagging by secondary objects and 2) automatic new landmark recognition. Images of 30 famous landmarks from various public databases were used in our experiment. Results show increments of tagged areas and the improvement of landmark tagging accuracy

Twitter usage in Tourism: Literature Review

Background: Twitter is the most popular microblog platform. Individuals, companies, organizations, and even governments use Twitter on a daily bases and get vast benefits from it. Twitter also has been valuable for the tourism sector, especially in developing business strategies, planning and studying tourist decision-making processes. Objectives: Goal of the paper is to identify the trends, patterns and the research gaps of the research focusing on the Twitter usage in tourism. Methods/Approach: A bibliometric analysis was conducted in order to identify significant authors, journals, and institutions who engaged in the research-oriented towards Twitter utilization in tourism. In addition, text-mining analysis has been conducted in order to extract and identify the topics of the papers investigating the utilization of Twitter for tourism research. Results: Research of Twitter utilization in tourism has increased substantially in the last decade, with most of the research conducted in the United States and Japan. Extracted topics are focused on distinctive themes, such as network analysis, word of mouth, and destination management. Conclusions: New topics have emerged, such as the utilization of Twitter in crisis communication and terrorist attacks, as well as the integration of Twitter and other social media such as Flickr

Discovering and characterizing places of interest using Flickr and Twitter

Databases of places have become increasingly popular to identify places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a broad coverage, there is a need for techniques that are capable of expanding place databases in an automated way. In this paper the authors discuss how geographically annotated information obtained from social media can be used to discover new places. In particular, the authors first determine potential places of interest by clustering the locations where Flickr photos have been taken. The tags from the Flickr photos and the terms of the Twitter messages posted in the vicinity of the obtained candidate places of interest are then used to rank them based on the likelihood that they belong to a given type. For several place types, their methodology finds places that are not yet contained in the databases used by Foursquare, Google, LinkedGeoData and Geonames. Furthermore, the authors' experimental results show that the proposed method can successfully identify errors in existing place databases such as Foursquare