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这就是朋友的作用:根据社交关系推断在线社交媒体平台上的位置

That’s What Friends Are For: Inferring Location in Online Social Media Platforms Based on Social Relationships
课程网址: https://videolectures.net/videos/icwsm2013_jurgens_social_relatio...  
主讲教师: David Jurgens
开课单位: 信息不详。欢迎您在右侧留言补充。
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
社交网络通常以空间局部性为基础,在这里,个人与附近遇到的人形成关系。然而,个人在在线社交网络平台上的位置往往是未知的。先前的方法试图从人们在网上发布的内容或他们在网上的关系中推断出个人的位置,但往往受到可用的位置相关数据的限制。我们提出了一种新的社交网络方法,通过在社交网络中传播空间位置分配,仅使用少量初始位置,就能准确地推断出几乎所有个体的位置。在五个实验中,我们展示了在多个社交网络平台上的有效性,使用精确和嘈杂的数据来启动推理,并提出了改进性能的启发式方法。在一个实验中,我们展示了推断一组用户位置的能力,这些用户每天产生超过74%的Twitter消息量,估计中位位置误差为10公里。我们的研究结果开启了从社交媒体平台收集大量位置注释数据的可能性。
课程简介: Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals’ locations from the content they produce online or their online relations, but often are limited by the available location-related data. We propose a new method for social networks that accurately infers locations for nearly all of individuals by spatially propagating location assignments through the social network, using only a small number of initial locations. In five experiments, we demonstrate the effectiveness in multiple social networking platforms, using both precise and noisy data to start the inference, and present heuristics for improving performance. In one experiment, we demonstrate the ability to infer the locations of a group of users who generate over 74% of the daily Twitter message volume with an estimated median location error of 10km. Our results open the possibility of gathering large quantities of location-annotated data from social media platforms.
关 键 词: 社交网络; 空间位置; 位置注释数据
课程来源: videolectures
数据采集: 2025-03-25:yuhongrui
最后编审: 2025-03-25:yuhongrui
阅读次数: 4