Stream volume prediction in twitter with artificial neural networks

Gabriela Dominguez, Juan Zamora, Miguel Guevara, H́ector Allende, Rodrigo Salas

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Twitter is one of the most important social network, where extracting useful information is of paramount importance to many application areas. Many works to date have tried to mine this information by taking the network structure, language itself or even by searching for a pattern in the words employed by the users. Anyway, a simple idea that might be useful for every challenging mining task - and that at out knowledge has not been tackled yet - consists of predicting the amount of messages (stream volume) that will be emitted in some specific time span. In this work, by using almost 180k messages collected in a period of one week, a preliminary analysis of the temporal structure of the stream volume in Twitter is made. The expected contribution consists of a model based on artificial neural networks to predict the amount of posts in a specific time window, which regards the past history and the daily behavior of the network in terms of the emission rate of the message stream.

Idioma originalInglés
Título de la publicación alojadaICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
Páginas488-493
Número de páginas6
EstadoPublicada - 2012
Publicado de forma externa
Evento1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012 - Vilamoura, Algarve, Portugal
Duración: 6 feb. 20128 feb. 2012

Serie de la publicación

NombreICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
Volumen2

Conferencia

Conferencia1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012
País/TerritorioPortugal
CiudadVilamoura, Algarve
Período6/02/128/02/12

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