Not all social media “friends” are close friends, but distinguishing them from mere acquaintances is an important task in marketing. The notion of a close friend is reflected in the metric tie strength, but the true tie strength is often unobserved in online social networks. With this research, we propose an approach that predicts real-world tie strength via online data measures of similarity, interaction, and network data. At its core, we assess ego network structures to predict tie strength, i.e., all first-degree connections and the interlinkage among them. Ego networks are easier to obtain than full networks, and researchers can process them more efficiently. We explain why bridging ego network positions could be associated with real-world tie strength and demonstrate the high discriminatory power of related network measures. In combination with measures of similarity and interaction, the precision of identifying all observed real-world strong ties is 45%. Finally, we empirically highlight the practical relevance of this finding by demonstrating that people react stronger to suggestions of a close friend compared to an acquaintance in a social advertisement experiment.

并非所有社交媒体“朋友”都是亲密朋友，但是将他们与单纯的熟人区分开是营销中的重要任务。亲密朋友的概念反映在度量联系强度上，但是真正的联系强度通常在在线社交网络中无法观察到。通过这项研究，我们提出了一种通过相似性，交互性和网络数据的在线数据量度来预测现实世界中领带强度的方法。在其核心，我们评估自我网络结构以预测联系强度，即所有一级连接及其之间的相互联系。自我网络比完整网络更容易获得，研究人员可以更有效地处理它们。我们解释了为何桥接自我网络的位置可以与现实世界的联系强度相关联，并证明相关网络措施具有很高的歧视性。结合相似性和交互性的度量，识别所有观察到的现实世界强关系的精度为45％。最后，我们通过证明与社交广告实验中的熟人相比，人们对亲密朋友的建议做出了更强烈的反应，从而从经验上强调了这一发现的实际意义。