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Conference Publication

Twitter User Geolocation using Deep Multiview Learning

Host Publication: IEEE International Conference on Acoustics, Speech and Signal Processing

Authors: T. Do, D. Nguyen, E. Tsiligianni, B. Cornelis and N. Deligiannis

Publisher: IEEE

Publication Date: Apr. 2018

Number of Pages: 5

ISBN: 9781538646588


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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Mr. Tien Do

+32 (0)2 629 2930

thdo@etrovub.be

more info

Mr. Duc Nguyen

+32 (0)02 629 168

mdnguyen@etrovub.be

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Dr. Evangelia Tsiligianni

+32 (0)02 629 168

etsiligi@etrovub.be

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Dr. Ir. Bruno Cornelis

+32 (0)02 629 167

bcorneli@etrovub.be

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Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

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