Country Localisation of Twitter Users

Jacky Casas, S. Berger, Omar Abou Khaled, E. Mugellini, D. Lalanne
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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.
Twitter用户的国家本土化
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。
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