A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

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.Comment: Accepted by EMNLP 201

Influence of geographic biases on geolocation prediction in Twitter

Geolocating Twitter users --- the task of identifying their home locations --- serves a wide range of community and business applications such as managing natural crises, journalism, and public health. While users can record their location on their profiles, more than 34% record fake or sarcastic locations. Twitter allows users to GPS locate their content, however, less than 1% of tweets are geotagged. Therefore, inferring user location has been an important field of investigation since 2010. This thesis investigates two of the most important factors which can affect the quality of inferring user location: (i) the influence of tweet-language; and (ii) the effectiveness of the evaluation process. Previous research observed that Twitter users writing in some languages appeared to be easier to locate than those writing in others. They speculated that the geographic coverage of a language (language bias) --- represented by the number of locations where the tweets of a specific language come from --- played an important role in determining location accuracy. So important was this role that accuracy might be largely predictable by considering language alone. In this thesis, I investigate the influence of language bias on the accuracy of geolocating Twitter users. The analysis, using a large corpus of tweets written in thirteen languages and a re-implemented state-of-the-art geolocation model back at the time, provides a new understanding of the reasons behind reported performance disparities between languages. The results show that data imbalance in the distribution of Twitter users over locations (population bias) has a greater impact on accuracy than language bias. A comparison between micro and macro averaging demonstrates that existing evaluation approaches are less appropriate than previously thought. The results suggest both averaging approaches should be used to effectively evaluate geolocation. Many approaches have been proposed for automatically geolocating users; at the same time, various evaluation metrics have been proposed to measure the effectiveness of these approaches, making it challenging to understand which of these metrics is the most suitable for this task. In this thesis, I provide a standardized evaluation framework for geolocation systems. The framework is employed to analyze fifteen Twitter user geolocation models and two baselines in a controlled experimental setting. The models are composed of the re-implemented model and a variation of it, two locally retrained open source models and the results of eleven models submitted to a shared task. Models are evaluated using ten metrics --- out of fourteen employed in previous research --- over four geographic granularities. Rank correlations and thorough statistical analysis are used to assess the effectiveness of these metrics. The results demonstrate that the choice of effectiveness metric can have a substantial impact on the conclusions drawn from a geolocation system experiment, potentially leading experimenters to contradictory results about relative effectiveness. For general evaluations, a range of performance metrics should be reported, to ensure that a complete picture of system effectiveness is conveyed. Although a lot of complex geolocation algorithms have been applied in recent years, a majority class baseline is still competitive at coarse geographic granularity. A suite of statistical analysis tests is proposed, based on the employed metric, to ensure that the results are not coincidental

A Neural Model for User Geolocation and Lexical Dialectology

We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods