一个准确的方式来交叉参考用户跨社交网络

J. J. Álvarez, Florina Almenárez Mendoza, Miguel Labrador
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引用次数: 6

摘要

社交网络允许人们拥有自己的虚拟身份,用来与其他在线用户互动。一个用户拥有多个在线个人资料,甚至是一个完全不同的匿名在线身份,这也是完全可能的,也并不罕见。有时需要揭开某些配置文件的匿名性,或者识别属于同一用户的两个不同配置文件。实体解析(ER)是匹配来自不同社交网络的两个不同的在线个人资料的任务。解决ER有很多好处,其中包括加强恐怖分子筛选,改进营销策略,消除重复的个人资料,识别虚假的个人资料等。我们的解决方案基于各种不同的属性,如用户名、真实姓名、位置、语言和其他类似的属性来比较配置文件。我们开发了各种字符串相似算法,并使用来自另一个项目的其他算法来比较配置文件,而不使用训练。我们开发的系统在大海捞针的场景中进行了测试,该系统的任务是匹配两个极其相似的配置文件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An accurate way to cross reference users across Social Networks
Social Networks have permitted people have their own virtual identities which they use to interact with other online users. It is also completely possible and not uncommon for a user to have more than one online profile or even a completely different anonymous online identity. Sometimes it is needed to unmask the anonymity of certain profiles, or to identify two difference profiles as belonging to the same user. Entity Resolution (ER) is the task of matching two different online profiles potentially from different social networks. Solving ER has a number of benefits, amongst them are enhanced terrorist screening, improved marketing strategies, elimination of duplicate profiles, identification of fake profiles, etc. Our solution compares profiles based on various different attributes such as their usernames, real names, locations, languages and other similar attributes. We developed various string similarity algorithms and used other algorithms from another project to compare profiles, without using training. The system we developed was tested in a needle-in-a-haystack scenario where the system was tasked with matching two profiles that were in a pool of extremely similar profiles.
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