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Mining urban events from the tweet stream through a probabilistic mixture model

Autor
Capdevila, J.; Cerquides, J.; Torres, J.
Tipus d'activitat
Article en revista
Revista
Data mining and knowledge discovery
Data de publicació
2018-05
Volum
32
Número
3
Pàgina inicial
764
Pàgina final
786
DOI
https://doi.org/10.1007/s10618-017-0541-y Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/111988 Obrir en finestra nova
URL
https://link.springer.com/article/10.1007%2Fs10618-017-0541-y Obrir en finestra nova
Resum
The geographical identification of content in Social Networks have enabled to bridge the gap between online social platforms and the physical world. Although vast amounts of data in such networks are due to breaking news or global occurrences, local events witnessed by users in situ are also present in these streams and of great importance for many city entities. Nowadays, unsupervised machine learning techniques, such as Tweet-SCAN, are able to retrospectively detect these local events from twe...
Citació
Capdevila, J., Cerquides, J., Torres, J. Mining urban events from the tweet stream through a probabilistic mixture model. "Data mining and knowledge discovery", 30 Agost 2017, p. 1-23.
Paraules clau
Event detection, Probabilistic models, Social networks, Variational inference
Grup de recerca
CAP - Grup de Computació d'Altes Prestacions

Participants