TY - GEN
T1 - How to Extract Relevant Knowledge from Tweets?
AU - Bouillot, Flavien
AU - Hai, Phan Nhat
AU - Béchet, Nicolas
AU - Bringay, Sandra
AU - Ienco, Dino
AU - Matwin, Stan
AU - Poncelet, Pascal
AU - Roche, Mathieu
AU - Teisseire, Maguelonne
PY - 2013
Y1 - 2013
N2 - Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we investigate two different problems. The first one is related to the extraction of representative terms from a set of tweets. More precisely we address the following question: are traditional information retrieval measures appropriate when dealing with tweets?. The second problem is related to the evolution of tweets over time for a set of users. With the development of data mining approaches, lots of very efficient methods have been defined to extract patterns hidden in the huge amount of data available. More recently new spatio-temporal data mining approaches have specifically been defined for dealing with the huge amount of moving object data that can be obtained from the improvement in positioning technology. Due to particularity of tweets, the second question we investigate is the following: are spatio-temporal mining algorithms appropriate for better understanding the behavior of communities over time? These two problems are illustrated through real applications concerning both health and political tweets.
AB - Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we investigate two different problems. The first one is related to the extraction of representative terms from a set of tweets. More precisely we address the following question: are traditional information retrieval measures appropriate when dealing with tweets?. The second problem is related to the evolution of tweets over time for a set of users. With the development of data mining approaches, lots of very efficient methods have been defined to extract patterns hidden in the huge amount of data available. More recently new spatio-temporal data mining approaches have specifically been defined for dealing with the huge amount of moving object data that can be obtained from the improvement in positioning technology. Due to particularity of tweets, the second question we investigate is the following: are spatio-temporal mining algorithms appropriate for better understanding the behavior of communities over time? These two problems are illustrated through real applications concerning both health and political tweets.
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U2 - 10.1007/978-3-642-40140-4_12
DO - 10.1007/978-3-642-40140-4_12
M3 - Conference contribution
AN - SCOPUS:84904905684
SN - 9783642401398
T3 - Communications in Computer and Information Science
SP - 111
EP - 120
BT - Information Search, Integration and Personalization - International Workshop, ISIP 2012, Revised Selected Papers
PB - Springer Verlag
T2 - 7th International Workshop on Information Search, Integration and Personalization, ISIP 2012
Y2 - 11 October 2012 through 13 October 2012
ER -