  Event detection in a multimodal Twitter dataset is considered. We treat the
hashtags in the dataset as instances with two modes: text and geolocation
features. The text feature consists of a bag-of-words representation. The
geolocation feature consists of geotags (i.e., geographical coordinates) of the
tweets. Fusing the multimodal data we aim to detect, in terms of topic and
geolocation, the interesting events and the associated hashtags. To this end, a
generative latent variable model is assumed, and a generalized
expectation-maximization (EM) algorithm is derived to learn the model
parameters. The proposed method is computationally efficient, and lends itself
to big datasets. Experimental results on a Twitter dataset from August 2014
show the efficacy of the proposed method.
