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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)
Brogueira, G., Batista, F. & Carvalho, J. P. (2016). Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions. Social Network Analysis and Mining. 6 (1)
Exportar Referência (IEEE)
G. M. Brogueira et al.,  "Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions", in Social Network Analysis and Mining, vol. 6, no. 1, 2016
Exportar BibTeX
@article{brogueira2016_1714033966884,
	author = "Brogueira, G. and Batista, F. and Carvalho, J. P.",
	title = "Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions",
	journal = "Social Network Analysis and Mining",
	year = "2016",
	volume = "6",
	number = "1",
	doi = "10.1007/s13278-016-0347-8",
	url = "http://link.springer.com/article/10.1007%2Fs13278-016-0347-8"
}
Exportar RIS
TY  - JOUR
TI  - Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions
T2  - Social Network Analysis and Mining
VL  - 6
IS  - 1
AU  - Brogueira, G.
AU  - Batista, F.
AU  - Carvalho, J. P.
PY  - 2016
SN  - 1869-5450
DO  - 10.1007/s13278-016-0347-8
UR  - http://link.springer.com/article/10.1007%2Fs13278-016-0347-8
AB  - The information published by the millions of public social network users is an important source of knowledge that can be used in academic, socioeconomic or demographic studies (distribution of male and female population, age, marital status, birth), lifestyle analysis (interests, hobbies, social habits) or be used to study online behavior (time spent online, interaction with friends or discussion about brands, products or politics). This work uses a database of about 27 million Portuguese geolocated tweets, produced in Portugal by 97.8 K users during a 1-year period, to extract information about the behavior of the geolocated Portuguese Twitter community and show that with this information it is possible to extract overall indicators such as: the daily periods of increased activity per region; prediction of regions where the concentration of the population is higher or lower in certain periods of the year; how do regional habitants feel about life; or what is talked about in each region. We also analyze the behavior of the geolocated Portuguese Twitter users based on the tweeted contents, and find indications that their behavior differs in certain relevant aspect from other Twitter communities, hypothesizing that this is in part due to the abnormal high percentage of young teenagers in the community. Finally, we present a small case study on Portuguese tourism in the Algarve region. To the best of our knowledge, this work is the first study that shows geolocated Portuguese users' behavior in Twitter focusing on geographic regional use.
ER  -