Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction

Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma

Abstract: We propose a novel geolocation prediction model using a complex neural network. Geolocation prediction in social media has attracted many researchers to use information of various types. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8% increase in accuracy and a maximum of 6.6% increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.