Twitter用户的国家本土化

Jacky Casas, S. Berger, Omar Abou Khaled, E. Mugellini, D. Lalanne
{"title":"Twitter用户的国家本土化","authors":"Jacky Casas, S. Berger, Omar Abou Khaled, E. Mugellini, D. Lalanne","doi":"10.1109/ICICS52457.2021.9464545","DOIUrl":null,"url":null,"abstract":"Localising Twitter users when trying to analyse local trends, events, or mood is a useful capability. However, there is still no method able to reach high precision and recall. Research projects attempting to localise Twitter users to a precise radius (e.g., 10km) managed to localise at most 60% of users correctly. In this paper, we propose a way to classify them by the country they are located in, instead of finding a precise localisation. We apply our technique to Switzerland and locate the users to inside or outside of the country. Among different features, we used relations of users to a list of \"Swiss Influencers\" accounts - that is, accounts which are mostly of interest to Swiss people. A full classification pipeline was implemented and tested. We have found that our best classification models achieved an accuracy of 95%, with a maximum precision of 98%, and a maximum recall of 91%. This goes to show that our binary classification problem, while potentially not being specific enough for certain types of applications, can amount to significantly more reliable results.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Country Localisation of Twitter Users\",\"authors\":\"Jacky Casas, S. Berger, Omar Abou Khaled, E. Mugellini, D. Lalanne\",\"doi\":\"10.1109/ICICS52457.2021.9464545\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Localising Twitter users when trying to analyse local trends, events, or mood is a useful capability. However, there is still no method able to reach high precision and recall. Research projects attempting to localise Twitter users to a precise radius (e.g., 10km) managed to localise at most 60% of users correctly. In this paper, we propose a way to classify them by the country they are located in, instead of finding a precise localisation. We apply our technique to Switzerland and locate the users to inside or outside of the country. Among different features, we used relations of users to a list of \\\"Swiss Influencers\\\" accounts - that is, accounts which are mostly of interest to Swiss people. A full classification pipeline was implemented and tested. We have found that our best classification models achieved an accuracy of 95%, with a maximum precision of 98%, and a maximum recall of 91%. This goes to show that our binary classification problem, while potentially not being specific enough for certain types of applications, can amount to significantly more reliable results.\",\"PeriodicalId\":421803,\"journal\":{\"name\":\"2021 12th International Conference on Information and Communication Systems (ICICS)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 12th International Conference on Information and Communication Systems (ICICS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICS52457.2021.9464545\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 12th International Conference on Information and Communication Systems (ICICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICS52457.2021.9464545","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在分析当地趋势、事件或情绪时,对Twitter用户进行本地化是一项有用的功能。然而,目前还没有一种方法能够达到高精度和召回率。研究项目试图将Twitter用户定位到一个精确的半径(例如,10公里),成功地定位了最多60%的用户。在本文中,我们提出了一种根据他们所在的国家对他们进行分类的方法,而不是寻找精确的定位。我们将我们的技术应用到瑞士,并将用户定位到瑞士境内或境外。在不同的功能中,我们使用了用户与“瑞士影响者”账户列表的关系,即瑞士人最感兴趣的账户。实现并测试了一个完整的分类管道。我们发现我们最好的分类模型达到了95%的准确率,最大精度为98%,最大召回率为91%。这表明,我们的二元分类问题虽然可能对某些类型的应用程序不够具体,但可以产生更可靠的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Country Localisation of Twitter Users
Localising Twitter users when trying to analyse local trends, events, or mood is a useful capability. However, there is still no method able to reach high precision and recall. Research projects attempting to localise Twitter users to a precise radius (e.g., 10km) managed to localise at most 60% of users correctly. In this paper, we propose a way to classify them by the country they are located in, instead of finding a precise localisation. We apply our technique to Switzerland and locate the users to inside or outside of the country. Among different features, we used relations of users to a list of "Swiss Influencers" accounts - that is, accounts which are mostly of interest to Swiss people. A full classification pipeline was implemented and tested. We have found that our best classification models achieved an accuracy of 95%, with a maximum precision of 98%, and a maximum recall of 91%. This goes to show that our binary classification problem, while potentially not being specific enough for certain types of applications, can amount to significantly more reliable results.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信