基于对比学习的社交媒体地理位置预测方法

徐永昌; 黄士多; 艾浩军

doi:10.11959/j.issn.1000-0801.2023154

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基于对比学习的社交媒体地理位置预测方法

研究与开发 | 更新时间：2024-06-05

- 基于对比学习的社交媒体地理位置预测方法
- A social media geolocation method based on comparative learning
- 电信科学 2023年39卷第8期页码：58-68
- 作者机构：
  
  1. 空天信息安全与可信计算教育部重点实验室，武汉大学国家网络安全学院，湖北武汉 430072
  2. 武汉市互联网舆情研究中心，湖北武汉 430014
- 作者简介：
  
  [ "徐永昌（1998- ），男，武汉大学国家网络安全学院硕士生，主要研究方向为普适计算" ]
  [ "黄士多（1965- ），男，武汉市互联网舆情研究中心副研究员，主要研究方向为网络舆情、社交媒体分析等" ]
  [ "艾浩军（1972- ），男，博士，武汉大学国家网络安全学院副教授，主要研究方向为普适计算与室内定位" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61971316)
- DOI：10.11959/j.issn.1000-0801.2023154
  中图分类号： TP391.1
- 网络出版日期：2023-08，
  
  纸质出版日期：2023-08-20
- 稿件说明：
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徐永昌, 黄士多, 艾浩军. 基于对比学习的社交媒体地理位置预测方法[J]. 电信科学, 2023,39(8):58-68.

Yongchang XU, Shiduo HUANG, Haojun AI. A social media geolocation method based on comparative learning[J]. Telecommunications science, 2023, 39(8): 58-68.
徐永昌, 黄士多, 艾浩军. 基于对比学习的社交媒体地理位置预测方法[J]. 电信科学, 2023,39(8):58-68. DOI： 10.11959/j.issn.1000-0801.2023154.

Yongchang XU, Shiduo HUANG, Haojun AI. A social media geolocation method based on comparative learning[J]. Telecommunications science, 2023, 39(8): 58-68. DOI： 10.11959/j.issn.1000-0801.2023154.

摘要

以往基于社交媒体文本的定位方法主要集中在将文本语义空间映射到地理位置空间，忽略了文本之间的语义相关性和地理位置之间的距离相关性。提出了一种新的无监督多层次对比学习框架，并设计了 3 个对比学习模块：语义学习模块、位置学习模块和跨层次学习模块。首先利用Transformer编码器获取文本的语义表示，以无监督的对比学习方式，聚拢位置相近文本之间的语义表示和地理表示，随后进行有监督训练，输出地理位置分类或回归结果。在4个数据集上与5个基线模型的对比实验结果表明，该框架有效地提升了社交媒体地理定位的准确性。

Abstract

Previous work on social media text-based geolocation focused on mapping language semantic space to geospatial space

which ignores the semantic correlation between social media texts and the distance correlation between geographical locations.To take advantage of these correlations

mCLF

a new unsupervised multiple-level contrastive learning framework was proposed

three contrastive learning modules were designed: a semantic learning module

a location learning module

and a cross-learning module.Transformer encoder was used to obtain semantic representation of posts

utilizing unsupervised contrastive learning method to decrease the distance of semantic representations and location representations of posts with near locations

and then fine-tuned the model with supervised method for geographic location regression or classification outputs.Compared with five baseline methods

extensive experiments based on four datasets demonstrate the effectiveness of the proposed framework.

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references

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