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Twitter User Geolocation using Multivew Deep Learning

Abstract: 

Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model. Our approach, named Multi-Entry Neural Network (MENET), leverages the latest advances in deep learning and multiview learning. A realization of MENET with textual, network and metadata features results in an effective method for Twitter user geolocation, achieving the state of the art on two well-known datasets.

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Paper Details

Authors:
Submitted On:
20 April 2018 - 4:22am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Tien Huu Do
Paper Code:
3216
Document Year:
2018
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icassp_2018_twitter.pdf

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icassp_2018_twitter.pdf

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[1] , "Twitter User Geolocation using Multivew Deep Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3033. Accessed: Jul. 19, 2018.
@article{3033-18,
url = {http://sigport.org/3033},
author = { },
publisher = {IEEE SigPort},
title = {Twitter User Geolocation using Multivew Deep Learning},
year = {2018} }
TY - EJOUR
T1 - Twitter User Geolocation using Multivew Deep Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3033
ER -
. (2018). Twitter User Geolocation using Multivew Deep Learning. IEEE SigPort. http://sigport.org/3033
, 2018. Twitter User Geolocation using Multivew Deep Learning. Available at: http://sigport.org/3033.
. (2018). "Twitter User Geolocation using Multivew Deep Learning." Web.
1. . Twitter User Geolocation using Multivew Deep Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3033