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.
Do Huu, T, Nguyen, MD, Tsiligianni, E, Cornelis, B & Deligiannis, N 2018, Twitter User Geolocation using Deep Multiview Learning. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. vol. 2018-April, 8462191, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, IEEE, pp. 6304-6308, IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8462191
Do Huu, T., Nguyen, M. D., Tsiligianni, E., Cornelis, B., & Deligiannis, N. (2018). Twitter User Geolocation using Deep Multiview Learning. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 6304-6308). Article 8462191 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). IEEE. https://doi.org/10.1109/ICASSP.2018.8462191
@inproceedings{7407f22f4d144544b69e1233f44b10dd,
title = "Twitter User Geolocation using Deep Multiview 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. ",
keywords = "Deep learning, Feature learning, Multiview learning, Twitter user geolocation",
author = "{Do Huu}, Tien and Nguyen, {Minh Duc} and Evangelia Tsiligianni and Bruno Cornelis and Nikolaos Deligiannis",
year = "2018",
month = apr,
day = "15",
doi = "10.1109/ICASSP.2018.8462191",
language = "English",
isbn = "9781538646588",
volume = "2018-April",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "IEEE",
pages = "6304--6308",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
note = "IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ; Conference date: 15-04-2018 Through 20-04-2018",
url = "https://2018.ieeeicassp.org/",
}