Name
Abstract | ||
|---|---|---|
Human-generated textual data streams from services such as Twitter increasingly become geo-referenced. The spatial resolution of their coverage improves quickly, making them a promising instrument for sensing various aspects of evolution and dynamics of social systems. This work explores spacetime structures of the topical content of short textual messages in a stream available from Twitter in Ireland. It uses a streaming Latent Dirichlet Allocation topic model trained with an incremental variational Bayes method. The posterior probabilities of the discovered topics are post-processed with a spatial kernel density and subjected to comparative analysis. The identified prevailing topics are often found to be spatially contiguous. We apply Markov-modulated non-homogeneous Poisson processes to quantify a proportion of novelty in the observed abnormal patterns. A combined use of these techniques allows for real-time analysis of the temporal evolution and spatial variability of population's response to various stimuli such as large scale sportive, political or cultural events. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2063212.2063223 | GIS-LBSN |
Keywords | Field | DocType |
space-time dynamic,spatial variability,various stimulus,spatial resolution,short textual message,spatial kernel density,various aspect,temporal evolution,comparative analysis,real-time analysis,human-generated textual data stream,space time,non homogeneous poisson process,kernel density,posterior probability,real time,social system,latent dirichlet allocation | Data mining,Population,Data stream mining,Latent Dirichlet allocation,Computer science,Posterior probability,Novelty,Topic model,Bayes' theorem,Kernel density estimation | Conference |
Citations | PageRank | References |
44 | 2.58 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexei Pozdnoukhov | 1 | 216 | 18.87 |
| Christian Kaiser | 2 | 44 | 2.58 |