Multimodal Event Detection in Twitter Hashtag Networks

Yilmaz, Yasin; Hero, Alfred O.

doi:10.1007/s11265-016-1151-4

Multimodal Event Detection in Twitter Hashtag Networks

Journal Article · Fri Jul 01 00:00:00 EDT 2016 · Journal of Signal Processing Systems

DOI:https://doi.org/10.1007/s11265-016-1151-4· OSTI ID:1454761

Yilmaz, Yasin ^[1]; Hero, Alfred O. ^[2]

Univ. of Michigan, Ann Arbor, MI (United States). Department of Electrical Engineering and Computer Science; University of Michigan
Univ. of Michigan, Ann Arbor, MI (United States). Department of Electrical Engineering and Computer Science

In this study, 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. Lastly, experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002534

OSTI ID:: 1454761

Journal Information:: Journal of Signal Processing Systems, Journal Name: Journal of Signal Processing Systems Journal Issue: 2 Vol. 90; ISSN 1939-8018

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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97 MATHEMATICS AND COMPUTING
Event detection
Generative latent variable model
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Multimodal Event Detection in Twitter Hashtag Networks

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