Twitter User Geolocation using Deep Multiview Learning
This work addresses the challenge of user geolocation for social media analysis, but it is incremental as it combines existing feature types into a new model.
The paper tackles the problem of predicting Twitter users' geographical locations by proposing a unified model that integrates content-based, network-based, and metadata features, achieving state-of-the-art results on two well-known datasets.
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.