Designing socially active streets has long been a goal of urban planning, yet existing quantitative research largely measures pedestrian volume rather than the quality of social interactions. We hypothesize that street view imagery -- an inexpensive data source with global coverage -- contains latent social information that can be extracted and interpreted through established social science theory. As a proof of concept, we analyzed 2,998 street view images from 15 cities using a multimodal large language model guided by Mehta's taxonomy of passive, fleeting, and enduring sociability -- one illustrative example of a theory grounded in urban design that could be substituted or complemented by other sociological frameworks. We then used linear regression models, controlling for factors like weather, time of day, and pedestrian counts, to test whether the inferred sociability measures correlate with city-level place attachment scores from the World Values Survey and with environmental predictors (e.g., green, sky, and water view indices) derived from individual street view images. Results aligned with long-standing urban planning theory: the sky view index was associated with all three sociability types, the green view index predicted enduring sociability, and place attachment was positively associated with fleeting sociability. These results provide preliminary evidence that street view images can be used to infer relationships between specific types of social interactions and built environment variables. Further research could establish street view imagery as a scalable, privacy-preserving tool for studying urban sociability, enabling cross-cultural theory testing and evidence-based design of socially vibrant cities.