|Speaker :||Dimitrios Milioris|
|Nokia Bell Labs|
|Time:||4:30 pm - 5:00 pm|
|Location:||LINCS / EIT Digital|
In this talk we introduce a novel information propagation method in Twitter, while maintaining a low computational complexity. The proposed method first employs Joint Complexity, which is defined as the cardinality of a set of all distinct factors of a given string represented by suffix trees, to perform topic detection. Then based on the nature of the data, we apply the theory of Compressive Sensing to perform topic classification by recovering an indicator vector, while reducing significantly the amount of information from tweets. Based on the spatial correlation of tweets and the score matrices, we apply a framework to complete matrices from a small number of random sample scores. We exploit datasets in various languages collected by using the Twitter streaming API and achieve better classification accuracy when compared with state-of-the-art methods.