# Multimodal Event Detection in Twitter Hashtag Networks

arXiv: https://arxiv.org/abs/1601.00306 · HTML: https://mlsanity.com/paper/1601.00306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2016.

## Built on by (most cited first, 1 of 1)

- [Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models](https://mlsanity.com/paper/2108.12445/) (2021)

## Abstract (arXiv)

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.