Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, the authors analyze the benefits of carpooling from a new angle, posing it as a possible means for favoring social integration in the city by matching carpoolers taking into account some of their social characteristics. Building upon a recently introduced network-based approach to model ride-sharing opportunities, the authors define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, the authors provide corresponding optimal and computationally efficient solutions. The authors then demonstrate their approach on two data sets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.