Abstract
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.
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Notes
The issue of unknown number of events can be handled using the silhouette values.
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Acknowledgments
This work was funded in part by the Consortium for Verification Technology under Department of Energy National Nuclear Security Administration award number DE-NA0002534, and the Army Research Office (ARO) under grants W911NF-11-1-0391 and W911NF-12-1-0443.
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Yılmaz, Y., Hero, A.O. Multimodal Event Detection in Twitter Hashtag Networks. J Sign Process Syst 90, 185–200 (2018). https://doi.org/10.1007/s11265-016-1151-4
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DOI: https://doi.org/10.1007/s11265-016-1151-4