Geosocial Search

Abstract

Geographic search-where the user provides keywords and receives relevant locations depicted on a map-is a popular web application. Typically, such search is based on static geographic data. However, the abundant geotagged posts in microblogs such as Twitter and in social networks like Instagram, provide contemporary information that can be used to support geosocial search-geographic search based on user activities in social media. Such search can point out where people talk (or tweet) about different topics. For example, the search results may show where people refer to "jogging", to indicate popular jogging places. The difficulty in implementing such search is that there is no natural partition of the space into "documents" as in ordinary web search. Thus, it is not always clear how to present results and how to rank and filter results effectively. In this paper, we demonstrate a two-step process of first, quickly finding the relevant areas by using an arbitrary indexed partition of the space, and secondly, applying clustering on discovered areas, to present more accurate results. We introduce a system that utilizes geotagged posts in geographic search and illustrate how different ranking methods can be used, based on the proposed two-step search process. The system demonstrates the effectiveness and usefulness of the approach.

Mapping tourist hot spots in African cities based on Instagram images

“…Instagram images have been used to analyse tourist behaviour in various different cities. That said, in the majority of cases, these have been western cities, with long traditions of tourism: cities in the USA (Pat, Kanza, & Naaman, 2015), Europe (Loiseau, Djebali, Raimbault, Branchet, & Chareyron, 2017), Asia (Hu et al, 2015) or the Americas (Paül i Agustí, 2020). The present research adds the study of six African cities to the existing literature.…”