A Unified Neural Network Model for Geolocating

Ebrahimi, Mohammad; ShafieiBavani, Elaheh; Wong, Raymond E.; Chen, Fang

doi:10.18653/v1/k18-1005

Cited by 16 publications

(11 citation statements)

References 38 publications

(31 reference statements)

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“…integrate text and user profile metadata into a single model using convolutional neural networks, and their experiments show superior performance over stacked naive Bayes classifiers. Miura et al (2017); Ebrahimi et al (2018) incorporate user network connection information into their neural models, where they use network embeddings to represent users in a social network. also uses text and network feature together, but their approach is based on graph convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Huang¹,

Carley²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.

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Section: Related Workmentioning

confidence: 99%

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Huang¹,

Carley²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Similarly to our study, Rahimi et al (2015) exploited the mentions, even though they used them to build undirected graphs. Ebrahimi et al (2017Ebrahimi et al ( , 2018 also used mentions to create an undirected graph, that they pruned and fed into an embedding layer followed by an attention mechanism, in order to create a network representation.…”

Section: Related Workmentioning

confidence: 99%

“…These works represent the state-of-the-art benchmark with respect to the implementation of network views in the models. Other models (Ebrahimi et al, 2017(Ebrahimi et al, , 2018Do et al, 2018) also include metadata or other source of information.…”

Section: Related Workmentioning

confidence: 99%

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Dense Node Representation for Geolocation

Fornaciari

Hovy

2019

Proceedings of the 5th Workshop on Noisy User-Generated Text (W-Nut 2019)

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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.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Identifying Linguistic Areas for Geolocation

Fornaciari

Hovy

2019

Proceedings of the 5th Workshop on Noisy User-Generated Text (W-Nut 2019)

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Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions that can serve as labels. Most studies use some form of clustering to discretize the continuous coordinates (Han et al., 2016). However, the resulting regions do not always correspond to existing linguistic areas. Consequently, accuracy at 100 miles tends to be good, but degrades for finer-grained distinctions, when different linguistic regions get lumped together. We describe a new algorithm, Point-to-City (P2C), an iterative k-d tree-based method for clustering geographic coordinates and associating them with towns. We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with regular k-d tree labels. Even though P2C results in substantially more labels than the baseline, model accuracy increases significantly over using traditional labels at the fine-grained level, while staying comparable at 100 miles. The results suggest that identifying meaningful linguistic areas is crucial for improving geolocation at a fine-grained level.

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A Unified Neural Network Model for Geolocating

Cited by 16 publications

References 38 publications

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Dense Node Representation for Geolocation

Identifying Linguistic Areas for Geolocation

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