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.
|Number of pages||8|
|Journal||2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)|
|Publication status||Published - 19 Feb 2018|
|Event||11th International Conference on Software, Knowledge, Information Management and Applications - Malabe, Sri Lanka|
Duration: 6 Dec 2017 → 8 Dec 2017
Cortesi, N., Gotti, K., Psaila, G., Burini, F., Lwin, K. T., & Hossain, A. (2018). A network-based ranking approach to discover places visited by tourists from geo-located tweets. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). https://doi.org/10.1109/SKIMA.2017.8294111