The geo-social relevance ranking: A method based on geographic information and social media data

摘要

A geographic search engine should be capable of identifying, indexing and retrieving the geographic context present in a considerable portion of the Web content, which is not expected in classic search engines. This type of search engine is characterised by a set of features, such as toponym recognition and resolution, geographic scope modelling, and relevance ranking. Ranking the results returned by a user query is a crucial task for this kind of system. In this paper, we present a method of ranking Web documents, which consider both the documents' geographic context and information collected from microblogs. This method also comprises an algorithm to estimate the affinity between locations and social network users, based on their interactions. The proposed method was evaluated within the news domain, using a system prototype which recognises geographic information contained in the news content and integrates information collected from Twitter.