Characterizing Geo-located Tweets in Brazilian Megacities

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Pereira, J (Author) Other; Pasquali, A (Author) Other; Saleiro, P (Author) Other; Rosaldo J. F. Rossetti (Author) FEUP; Cacho, N (Author) Other

Conference proceedings International

Title: 2017 International Smart Cities Conference, ISC2 2017

2017 International Smart Cities Conference, ISC2 2017

14 September 2017 through 17 September 2017

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-00N-2MC

DOI: 10.1109/isc2.2017.8090832

Abstract (EN): This work presents a framework for collecting, processing and mining geo-located tweets in order to extract meaningful and actionable knowledge in the context of smart cities. We collected and characterized more than 9M tweets from the two biggest cities in Brazil, Rio de Janeiro and Sao Paulo. We performed topic modeling using the Latent Dirichlet Allocation model to produce an unsupervised distribution of semantic topics over the stream of geo-located tweets as well as a distribution of words over those topics. We manually labeled and aggregated similar topics obtaining a total of 29 different topics across both cities. Results showed similarities in the majority of topics for both cities, reflecting similar interests and concerns among the population of Rio de Janeiro and Sao Paulo. Nevertheless, some specific topics are more predominant in one of the cities. © 2017 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6