TY - GEN
T1 - Stream volume prediction in twitter with artificial neural networks
AU - Dominguez, Gabriela
AU - Zamora, Juan
AU - Guevara, Miguel
AU - Allende, H́ector
AU - Salas, Rodrigo
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Stream volume prediction
KW - Time series forecasting
KW - Twitter analysis
UR - http://www.scopus.com/inward/record.url?scp=84862228306&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84862228306
SN - 9789898425980
T3 - ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
SP - 488
EP - 493
BT - ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
T2 - 1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012
Y2 - 6 February 2012 through 8 February 2012
ER -