14 research outputs found
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation
Accurate estimation of user location is important for many online services.
Previous neural network based methods largely ignore the hierarchical structure
among locations. In this paper, we propose a hierarchical location prediction
neural network for Twitter user geolocation. Our model first predicts the home
country for a user, then uses the country result to guide the city-level
prediction. In addition, we employ a character-aware word embedding layer to
overcome the noisy information in tweets. With the feature fusion layer, our
model can accommodate various feature combinations and achieves
state-of-the-art results over three commonly used benchmarks under different
feature settings. It not only improves the prediction accuracy but also greatly
reduces the mean error distance.Comment: Accepted by EMNLP 201
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
Real-time location inference of social media users is the fundamental of some
spatial applications such as localized search and event detection. While tweet
text is the most commonly used feature in location estimation, most of the
prior works suffer from either the noise or the sparsity of textual features.
In this paper, we aim to tackle these two problems. We use topic modeling as a
building block to characterize the geographic topic variation and lexical
variation so that "one-hot" encoding vectors will no longer be directly used.
We also incorporate other features which can be extracted through the Twitter
streaming API to overcome the noise problem. Experimental results show that our
RATE algorithm outperforms several benchmark methods, both in the precision of
region classification and the mean distance error of latitude and longitude
regression.Comment: 4 pages; Accepted to CIKM 2017; Some typos fixe
The role of different tie strength in disseminating different topics on a microblog
The aim of this paper is to construct higher order approximate discrete time filters for continuous time finite-state Markov chains with observations that are perturbed by the noise of a Wiener process