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Authors: Gabriela Dominguez 1 ; Juan Zamora 1 ; Miguel Guevara 2 ; Héctor Allende 1 and Rodrigo Salas 3

Affiliations: 1 Universidad Técnica Federico Santa María, Chile ; 2 Universidad Técnica Federico Santa María and Universidad de Playa Ancha, Chile ; 3 Universidad de Valparaíso, Chile

ISBN: 978-989-8425-99-7

Keyword(s): Twitter analysis, Stream volume prediction, Artificial neural networks, Time series forecasting.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Computational Intelligence ; Data Engineering ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Information Retrieval ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Ontologies and the Semantic Web ; Pattern Recognition ; Physiological Computing Systems ; Regression ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Engineering ; Theory and Methods

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.

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Paper citation in several formats:
Dominguez, G.; Zamora, J.; Guevara, M.; Allende, H. and Salas, R. (2012). STREAM VOLUME PREDICTION IN TWITTER WITH ARTIFICIAL NEURAL NETWORKS.In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-99-7, pages 488-493. DOI: 10.5220/0003837004880493

@conference{icpram12,
author={Gabriela Dominguez. and Juan Zamora. and Miguel Guevara. and Héctor Allende. and Rodrigo Salas.},
title={STREAM VOLUME PREDICTION IN TWITTER WITH ARTIFICIAL NEURAL NETWORKS},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={488-493},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003837004880493},
isbn={978-989-8425-99-7},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - STREAM VOLUME PREDICTION IN TWITTER WITH ARTIFICIAL NEURAL NETWORKS
SN - 978-989-8425-99-7
AU - Dominguez, G.
AU - Zamora, J.
AU - Guevara, M.
AU - Allende, H.
AU - Salas, R.
PY - 2012
SP - 488
EP - 493
DO - 10.5220/0003837004880493

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