Stream volume prediction in twitter with artificial neural networks

Gabriela Dominguez, JUAN FRANCISCO ZAMORA OSORIO, Miguel Guevara, H́ector Allende, Rodrigo Salas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
Pages488-493
Number of pages6
StatePublished - 18 Jun 2012
Event1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012 - Vilamoura, Algarve, Portugal
Duration: 6 Feb 20128 Feb 2012

Publication series

NameICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
Volume2

Conference

Conference1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012
Country/TerritoryPortugal
CityVilamoura, Algarve
Period6/02/128/02/12

Keywords

  • Artificial neural networks
  • Stream volume prediction
  • Time series forecasting
  • Twitter analysis

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