While direct social ties have been intensely studied in the context of computer-mediated social networks, indirect ties (e.g., friends of friends) have seen little attention. Yet in real life, we often rely on friends of our friends for recommendations (of good doctors, good schools, or good babysitters), for introduction to a new job opportunity, and for many other occasional needs. In this work we attempt to 1) quantify the strength of indirect social ties, 2) validate the quantification, and 3) empirically demonstrate its usefulness for applications on two examples. We quantify social strength of indirect ties using a measure of the strength of the direct ties that connect two people and the intuition provided by the sociology literature. We evaluate the proposed metric by framing it as a link prediction problem and experimentally demonstrate that our metric accurately (up to 87.2%) predicts link’s formation. We show via data-driven experiments that the proposed metric for social strength can be used successfully for social applications. Specifically, we show that it can be used for predicting the effects of information diffusion with an accuracy of up to 0.753. We also show that it alleviates known problems in friend-to-friend storage systems by addressing two previously documented shortcomings: reduced set of storage candidates and data availability correlations. © 2015 Elsevier B.V. All rights reserved.