Friendship Prediction and Homophily in Social Media
Friendship Prediction and Homophily in Social Media
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DOI：
10.1145/2180861.2180866
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发表时间：
2012-05-01
影响因子：
3.5
通讯作者：
Menczer, Filippo
中科院分区：
文献类型：
--
作者：
Aiello, Luca Maria;Barrat, Alain;Menczer, Filippo
社交媒体吸引了相当多的关注，因为它们的开放性允许用户创建轻量级的语义脚手架来组织和共享内容。到目前为止，社会媒体的社会和专题组成部分的相互作用只得到了部分探讨。在这里，我们研究的同质性存在于三个系统，结合联合收割机标记社会媒体与在线社交网络。我们发现，在社交网络中彼此接近的用户之间存在相当程度的主题相似性。我们引入了一个空模型，保留用户活动，同时删除本地相关性，使我们能够解开用户之间的实际本地相似性的统计效果，由于在社交网络中的用户活动和中心的强制混合。该分析表明，具有相似兴趣的用户更有可能成为朋友，因此仅基于其注释元数据的用户之间的主题相似性度量应该预测社交链接。我们在几个数据集上测试了这一假设，证实了从主题相似性构建的社交网络准确地捕捉了实际的友谊。当结合拓扑特征时，主题相似度达到约92%的链接预测准确率。
Social media have attracted considerable attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and topical components of social media has been only partially explored. Here, we study the presence of homophily in three systems that combine tagging social media with online social networks. We find a substantial level of topical similarity among users who are close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local similarity between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on several datasets, confirming that social networks constructed from topical similarity capture actual friendship accurately. When combined with topological features, topical similarity achieves a link prediction accuracy of about 92%.