Twitter Geolocation Prediction Using Neural Networks

Thomas, Philippe; Hennig, Leonhard

doi:10.1007/978-3-319-73706-5_21

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…This model has been further extended by Miura et al (2017) to also consider user network information for geolocation. Thomas and Hennig (2017) have proposed a geolocation method that relies on the combination of individual neural networks trained on text and metadata fields. Ebrahimi et al (2018a) have proposed a word embedding-based approach to predict the geographic proximity of connected users in the social graph based on their linguistic similarities.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A Unified Neural Network Model for Geolocating

Ebrahimi¹,

ShafieiBavani²,

Wong³

et al. 2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation. However, many users do not share their exact geographical coordinates due to reasons such as privacy concerns. The lack of explicit location information has motivated a growing body of research in recent years looking at different automatic ways of determining the user's primary location. In this paper, we propose a unified user geolocation method which relies on a fusion of neural networks. Our joint model incorporates different types of available information including tweet text, user network, and metadata to predict users' locations. Moreover, we utilize a bidirectional LSTM network augmented with an attention mechanism to identify the most location indicative words in textual content of tweets. The experiments demonstrate that our approach achieves state-of-the-art performance over two Twitter benchmark geolocation datasets. We also conduct an ablation study to evaluate the contribution of each type of information in user geolocation performance.

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Section: Hybrid Methodsmentioning

confidence: 99%

A Unified Neural Network Model for Geolocating

Ebrahimi¹,

ShafieiBavani²,

Wong³

et al. 2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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“…In particular, the lack of dynamic and geo tagged opinion data. However, in the case of Twitter, other researches have proposed approaches to infer the locations of messages when lacking this information [18], [53]. Furthermore, to the best of our knowledge, there were few public geo tagged free text datasets that we could exploit.…”

Section: B Limitationsmentioning

confidence: 99%

Geo-Spatial Market Segmentation & Characterization Exploiting User Generated Text Through Transformers & Density-Based Clustering

et al. 2021

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“…As only a small number of Tweets contain country related information (8.63%), Tweets lacking this information had to be assigned automatically to the corresponding country of each user. A classifier was trained based on the approach of Thomas and Hennig (2018), which is able to detect the origin of a Tweet based on text and meta information.…”

Section: Country Assignmentmentioning

confidence: 99%

Football and Beer - a Social Media Analysis on Twitter in Context of the FIFA Football World Cup 2018

Roller

Thomas

Schmeier

2018

Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop &Amp; Shared Task

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In many societies alcohol is a legal and common recreational substance and socially accepted. Alcohol consumption often comes along with social events as it helps people to increase their sociability and to overcome their inhibitions. On the other hand we know that increased alcohol consumption can lead to serious health issues, such as cancer, cardiovascular diseases and diseases of the digestive system, to mention a few. This work examines alcohol consumption during the FIFA Football World Cup 2018, particularly the usage of alcohol related information on Twitter. For this we analyse the tweeting behaviour and show that the tournament strongly increases the interest in beer. Furthermore we show that countries who had to leave the tournament at early stage might have done something good to their fans as the interest in beer decreased again.

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Twitter Geolocation Prediction Using Neural Networks

Cited by 13 publications

References 10 publications

A Unified Neural Network Model for Geolocating

A Unified Neural Network Model for Geolocating

Geo-Spatial Market Segmentation & Characterization Exploiting User Generated Text Through Transformers & Density-Based Clustering

Football and Beer - a Social Media Analysis on Twitter in Context of the FIFA Football World Cup 2018

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