在线社交网络中用户地理位置预测综述  

Survey of User Geographic Location Prediction Based on Online Social Network

作　　者：刘乐源 代雨柔 曹亚男[2] 周帆[1] Liu Leyuan;Dai Yurou;Cao Yanan;Zhou Fan(School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054;Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093)

机构地区：[1]电子科技大学信息与软件工程学院,成都610054 [2]中国科学院信息工程研究所,北京100093

出　　处：《计算机研究与发展》2024年第2期385-412,共28页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(62176043,62072077,62272089);四川省自然科学基金项目(2022NSFSC0505);四川省科技计划项目(2022YFSY0006)。

摘　　要：随着智能移动终端的日益普及,人们越来越多地利用社交网络平台(如Twitter、新浪微博等)获取信息、评论和交流.虽然全球卫星定位系统(GPS)设备能够精确获取位置信息,但是大量用户出于隐私和安全的考虑不会直接共享自己的位置信息.因此,如何获取在线用户的地理位置成为了一个前沿的研究领域以及学术界和工业界共同关注的重要课题,并且成为众多下游应用的基础,例如基于位置的定向广告投放、事件/地点的推荐、自然灾害或疾病预警和网络犯罪的追踪等.详细总结了预测社交网络用户地理位置的方法、数据、评价体系和基础算法.首先,归纳了不同的定位任务以及相应的评价指标;其次,针对不同的任务梳理所用的数据类型和数据融合方式,并且,详尽分析了已有的信息抽取和特征选择方式及其优缺点;再次,对现有定位模型和算法进行分类,从地名词典、传统机器学习和深度学习3个方面对用户定位方法进行阐述和分析;最后,总结了社交网络用户地理位置预测的难点和面临的挑战,并展望该领域的发展趋势和未来研究所需要关注的方向.With the increasing popularity of intelligent mobile terminals,people increasingly use social network platforms(such as Twitter,Sina Weibo,etc.)for information acquisition,comments,and exchanges.Although GPS devices can accurately obtain location information,many users do not directly share their location information for privacy and security considerations.Therefore,obtaining the geographic location of online users has become an important topic in both academia and industry and is the foundation of many downstream applications,such as location-based targeted advertising,event/location recommendations,early warning of natural disasters or diseases,and criminal tracking,etc.We survey in detail the methods,data types,evaluation metrics,and fundamental algorithms for predicting the geographic location of social network users.First,we discriminate different online user geolocation tasks and corresponding evaluation protocols.Subsequently,we assess the data structures and fusion methods used for individual geolocation tasks.Besides,we analyze the existing information extraction and feature selection approaches,as well as their advantages and disadvantages.Moreover,we provide a taxonomy to categorize existing user geolocation models and algorithms,followed by a thorough analysis of different methods from three aspects:geographic dictionary,traditional machine learning,deep learning and graph neural networks.Finally,we summarize the difficulties and challenges in user location prediction while outlining the possible research trend and opportunities to shed light on future work in this field.

关 键 词：社交网络 地理位置预测 数据分析与融合 信息抽取 时空特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]