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Fine-Grained Geolocalization of User-Generated Short Text based on Weight Probability Model

Authors:

Congjie Gao,

Yongjnu Li,

Jiaqi YangAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2089 - 2092

https://doi.org/10.1145/3357384.3358137

Published: 03 November 2019 Publication History

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Abstract

Recently, the fine-grained geolocalization of User-Generated Short Text (UGST) has become increasingly important. Existing methods can not make full use of the location information in the UGSTs. Besides, existing works only consider the importance of terms for all locations, but do not distinguish the importance of the same term in different locations. To solve these problems, we propose a fine-grained geolocalization method based on a weight probability model (FGST-WP). The method mainly includes three parts: 1) Using the reverse maximum match algorithm to filter out UGSTs that do not contain any location indicative information. 2) Building coupling of terms and locations and adopting a mixed weight strategy to assign weights to terms. 3) Calculating the probability of non-geotagged UGST posted from each location and selecting k locations according to the top-k probabilities. Experiments on ground-truth datasets prove the superior performance of FGST-WP.

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Cited By

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Gao CLi YYang JZhang Y(2022)A Location Recall Strategy for Improving Efficiency of User-Generated Short Text GeolocalizationIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31163419:5(1419-1431)Online publication date: Oct-2022
https://doi.org/10.1109/TCSS.2021.3116341
Zhang YLi YJi WWang S(2022)A Fine‐Grained Geolocalization Method for User Generated Short TextIEEJ Transactions on Electrical and Electronic Engineering10.1002/tee.2365917:10(1485-1494)Online publication date: 24-Jun-2022
https://doi.org/10.1002/tee.23659

Index Terms

Fine-Grained Geolocalization of User-Generated Short Text based on Weight Probability Model
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms

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Published In

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

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Published: 03 November 2019

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Natural Science Basic Research Plan in Shaanxi Province of China

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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Gao CLi YYang JZhang Y(2022)A Location Recall Strategy for Improving Efficiency of User-Generated Short Text GeolocalizationIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31163419:5(1419-1431)Online publication date: Oct-2022
https://doi.org/10.1109/TCSS.2021.3116341
Zhang YLi YJi WWang S(2022)A Fine‐Grained Geolocalization Method for User Generated Short TextIEEJ Transactions on Electrical and Electronic Engineering10.1002/tee.2365917:10(1485-1494)Online publication date: 24-Jun-2022
https://doi.org/10.1002/tee.23659

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Exploiting User and Venue Characteristics for Fine-Grained Tweet Geolocation

A Fine-Grained Indoor Location-Based Social Network

Effective fine-grained location prediction based on user check-in pattern in LBSNs

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