VacationFinder: a tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots

Abstract

Choosing a location for vacations and weekends usually confuses many people. This concern has attracted considerable attention in recent years as currently there is no application based on actual visitors that helps people in finding out the top places for vacations. Online social networks such as Twitter are becoming very popular in last few years and can help in this regard. People nowadays generally do check-ins at new places. Also, analysis of tweets tagged with geolocation and time can provide trends of top vacation spots. In this paper, we present VacationFinder; a novel location-based application that uses geotagged tweets to help people in where they should spend their holidays and weekends. We use real Twitter data crawled since October 2013. We apply indexing, spatio-temporal querying, and machine learning techniques to check, analyze, and filter the user activities in a particular country before and after a specific holiday. We then visualize the results and give our recommendations of top vacation spots for a particular holiday. The paper includes use cases on top vacation spots for Saudis in spring break of 2014 both inside as well as outside Saudi Arabia. Our application can not only help people but can also give direction to governmental agencies about promoting tourism in the country. It can also help law enforcement agencies, advertisement industry, and various businesses such as restaurants and shopping stores about where to focus during a particular holiday.