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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Carvalho, J. P., Rosa, H. & Batista, F. (2017). Detecting relevant tweets in very large tweet collections: the London Riots case study. In 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017. Naples: IEEE.
Exportar Referência (IEEE) 
J. P. Carvalho et al.,  "Detecting relevant tweets in very large tweet collections: the London Riots case study", in 2017 IEEE Int. Conf. on Fuzzy Systems, FUZZ 2017, Naples, IEEE, 2017
Exportar BibTeX 
@inproceedings{carvalho2017_1714848689654,
	author = "Carvalho, J. P. and Rosa, H. and Batista, F.",
	title = "Detecting relevant tweets in very large tweet collections: the London Riots case study",
	booktitle = "2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017",
	year = "2017",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/FUZZ-IEEE.2017.8015635",
	publisher = "IEEE",
	address = "Naples",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/8015635/"
}
Exportar RIS 
TY  - CPAPER
TI  - Detecting relevant tweets in very large tweet collections: the London Riots case study
T2  - 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
AU  - Carvalho, J. P.
AU  - Rosa, H.
AU  - Batista, F.
PY  - 2017
SN  - 1098-7584
DO  - 10.1109/FUZZ-IEEE.2017.8015635
CY  - Naples
UR  - https://ieeexplore.ieee.org/document/8015635/
AB  - In this paper we propose to approach the subject of detecting relevant tweets when in the presence of very large tweet collections containing a large number of different trending topics. We use a large database of tweets collected during the 2011 London Riots as a case study to demonstrate the application of the proposed techniques. In order to extract relevant content, we extend, formalize and apply a recent technique, called Twitter Topic Fuzzy Fingerprints, which, in the scope of social media, outperforms other well known text based classification methods, while being less computationally demanding, an essential feature when processing large volumes of streaming data. Using this technique we were able to detect 45% additional relevant tweets within the database.
ER  -