基于多模态融合的社交媒体文本地理位置预测方法

黄士多; 徐永昌; 艾浩军

doi:10.11959/j.issn.1000-0801.2023183

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基于多模态融合的社交媒体文本地理位置预测方法

研究与开发 | 更新时间：2024-06-05

- 基于多模态融合的社交媒体文本地理位置预测方法
- A social media geolocation prediction method based on multimodal fusion
- 电信科学 2023年39卷第9期页码：111-121
- 作者机构：
  
  1. 武汉市互联网舆情研究中心，湖北武汉 430014
  2. 武汉大学国家网络安全学院，湖北武汉 430072
- 作者简介：
  
  [ "黄士多（1965- ），男，武汉市互联网舆情研究中心副研究员、主任，主要研究方向为网络舆情和社交媒体分析" ]
  [ "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" ]
  [ "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和室内定位" ]
- 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.1000-0801.2023183
  中图分类号： TP391.1
- 网络出版日期：2023-08，
  
  纸质出版日期：2023-08-25
- 稿件说明：
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黄士多, 徐永昌, 艾浩军. 基于多模态融合的社交媒体文本地理位置预测方法[J]. 电信科学, 2023,39(9):111-121.

Shiduo HUANG, Yongchang XU, Haojun AI. A social media geolocation prediction method based on multimodal fusion[J]. Telecommunications science, 2023, 39(9): 111-121.
黄士多, 徐永昌, 艾浩军. 基于多模态融合的社交媒体文本地理位置预测方法[J]. 电信科学, 2023,39(9):111-121. DOI： 10.11959/j.issn.1000-0801.2023183.

Shiduo HUANG, Yongchang XU, Haojun AI. A social media geolocation prediction method based on multimodal fusion[J]. Telecommunications science, 2023, 39(9): 111-121. DOI： 10.11959/j.issn.1000-0801.2023183.

摘要

挖掘社交媒体文本的地理位置信息能发现其空间关系，提出了基于多模态融合的社交媒体文本地理位置预测方法，利用文本获取的相关图片作为增强数据，构建图文数据集，以提高地理位置预测的准确性。多模态融合模型分别利用图片通道和文本通道提取两者的地理位置信息。同时，引入图文匹配模块对图文对进行降噪，解决图文不匹配问题。在Geotext数据集上进行的地理位置预测实验结果显示，与基线模型相比，中值误差距离降低了18.8%，平均误差距离降低了4.5%。

Abstract

Geographical information extracted from social media text reveals underlying spatial correlations.A geographical location prediction method for social media text based on multimodal fusion was proposed.By utilizing images associated with the text as augmented data

an integrated image-text dataset was constructed to enhance the accuracy of geographical location prediction.The multimodal fusion model employs separate channels for images and text to independently extract their respective geographical location information.Additionally

a text-image matching module was introduced to denoise the image-text pairs

effectively solving the issue of text-image misalignment.Experimental results on the Geotext dataset indicate that compared to the baseline model

the proposed method reduces the median error distance by 18.8% and the average error distance by 4.5%.

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Keywords

references

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