Dense Node Representation for Geolocation

Abstract

Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of connections, which grow exponentially with the number of users. In order to incorporate this information, we therefore need to limit the network size, in turn limiting performance and risking sample bias. In this paper, we address these limitations by instead using dense network representations. We explore two methods to learn continuous node representations from either 1) the network structure with node2vec (Grover and Leskovec, 2016), or 2) textual user mentions via doc2vec (Le and Mikolov, 2014). We combine both methods with input from social media posts in an attention-based convolutional neural network and evaluate the contribution of each component on geolocation performance. Our method enables us to incorporate arbitrarily large networks in a fixed-length vector, without limiting the network size. Our models achieve competitive results with similar state-of-the-art methods, but with much fewer model parameters, while being applicable to networks of virtually any size.

Geolocation with Attention-Based Multitask Learning Models

“…Most successful recent approaches to geolocation use Deep Learning architectures for the task (Liu and Inkpen, 2015;Iso et al, 2017;Han et al, 2016). Many authors (Miura et al, 2016;Bakerman et al, 2018;Rahimi et al, 2018;Ebrahimi et al, 2018;Do et al, 2018;Fornaciari and Hovy, 2019a) follow a hybrid approach, combining the text representation with network information and further meta-data. However, recent works explore the effectiveness of purely textual data for geolocation (Tang et al, 2019).…”

Identifying Linguistic Areas for Geolocation

“…Most works use deep learning techniques for classification (Miura et al, 2016). Often, they include multi-view models, considering different sources (Miura et al, 2017;Lau et al, 2017;Ebrahimi et al, 2018;Fornaciari and Hovy, 2019a). In particular, Lau et al (2017) implemented a multi-channel convolutional network, structurally similar to our model.…”