在动态在线交互网络中检测友谊Detecting Friendship within Dynamic Online Interaction Network |
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课程网址: | https://videolectures.net/videos/icwsm2013_merritt_dynamic_online |
主讲教师: | Sears Merritt |
开课单位: | 信息不详。欢迎您在右侧留言补充。 |
开课时间: | 2014-04-03 |
课程语种: | 英语 |
中文简介: | 在许多复杂的社会系统中,个人之间互动的时间和频率是可以观察到的,但友谊纽带是隐藏的。在这里,我们研究了多个统计特征的准确性,这些特征要么纯粹基于时间交互模式,要么基于交互的合作性质,用于自动提取潜在的社会联系。使用从匿名在线调查中得出的自我报告的友谊和非友谊标签,我们学习了高度准确的预测器,用于在包含流行在线游戏 Halo: Reach 的 1700 万个体之间的 180 亿次互动的海量在线数据集中恢复隐藏的友谊。我们发现交互时间序列中的周期性足以正确分类 95% 的平局,即使对于临时用户也是如此。这些结果阐明了在线社交环境中友谊的性质,并为不需要披露私人友谊信息的友谊感知应用程序提出了新的机会和新的隐私问题。 |
课程简介: | In many complex social systems, the timing and frequency of interactions between individuals are observable but friendship ties are hidden. Here, we investigate the accuracy of multiple statistical features, based either purely on temporal interaction patterns or on the cooperative nature of the interactions, for automatically extracting latent social ties. Using self-reported friendship and non-friendship labels derived from an anonymous online survey, we learn highly accurate predictors for recovering hidden friendships within a massive online data set encompassing 18 billion interactions among 17 million individuals of the popular online game Halo: Reach. We find that periodicities in interaction time series are sufficient to correctly classify 95% of ties, even for casual users. These results clarify the nature of friendship in online social environments and suggest new opportunities and new privacy concerns for friendship-aware applications that do not require the disclosure of private friendship information. |
关 键 词: | 友谊; 社会联系; 交互模式 |
课程来源: | videolectures |
数据采集: | 2025-05-16:yuhongrui |
最后编审: | 2025-05-16:yuhongrui |
阅读次数: | 2 |