A network-based ranking approach to discover places visited by tourists from geo-located tweets

Nicola Cortesi, Kevin Gotti, Giuseppe Psaila, Federica Burini, K T Lwin, Alamgir Hossain

Research output: Contribution to journalConference article

Abstract

This work analyses the existing connections between public spaces in the city, by developing a new ranking method based on the information related to citizens' movement in the urban space using social media. We propose a NodeRank algorithm, a modified version of the Page-Rank algorithm, which introduces a new reticular perspective as it considers both incoming links in a page, and outgoing links too. The proposed algorithm has been tested with a dataset of geolocated Tweets collected in previous research. Results indicate that the proposed Node-Rank Algorithm offers an excellent performance in identifying the places of greatest interest from the point of view of Twitter users and it is useful to reconstruct the network between public spaces in the city.

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