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 pl...
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sg-ntu-dr.10356-859892020-03-07T11:48:58Z A survey of location prediction on twitter Zheng, Xin Han, Jialong Sun, Aixin School of Computer Science and Engineering Tweets Twitter DRNTU::Engineering::Computer science and engineering 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 make a conclusion of the survey and list future research directions. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) EDB (Economic Devt. Board, S’pore) Accepted version 2019-05-17T08:42:17Z 2019-12-06T16:13:56Z 2019-05-17T08:42:17Z 2019-12-06T16:13:56Z 2018 Journal Article Zheng, X., Han, J., & Sun, A. (2018). A Survey of Location Prediction on Twitter. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1652-1671. doi:10.1109/TKDE.2018.2807840 1041-4347 https://hdl.handle.net/10356/85989 http://hdl.handle.net/10220/48273 10.1109/TKDE.2018.2807840 en IEEE Transactions on Knowledge and Data Engineering © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2018.2807840. 20 p. application/pdf |
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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 make a conclusion of the survey and list future research directions. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Zheng, Xin Han, Jialong Sun, Aixin |
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Article |
author |
Zheng, Xin Han, Jialong Sun, Aixin |
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Zheng, Xin |
title |
A survey of location prediction on twitter |
title_short |
A survey of location prediction on twitter |
title_full |
A survey of location prediction on twitter |
title_fullStr |
A survey of location prediction on twitter |
title_full_unstemmed |
A survey of location prediction on twitter |
title_sort |
survey of location prediction on twitter |
publishDate |
2019 |
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https://hdl.handle.net/10356/85989 http://hdl.handle.net/10220/48273 |
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