  Sociologists associate the spatial variation of crime within an urban
setting, with the concept of collective efficacy. The collective efficacy of a
neighborhood is defined as social cohesion among neighbors combined with their
willingness to intervene on behalf of the common good. Sociologists measure
collective efficacy by conducting survey studies designed to measure
individuals' perception of their community. In this work, we employ the curated
data from a survey study (ground truth) and examine the effectiveness of
substituting costly survey questionnaires with proxies derived from social
media. We enrich a corpus of tweets mentioning a local venue with several
linguistic and topological features. We then propose a pairwise learning to
rank model with the goal of identifying a ranking of neighborhoods that is
similar to the ranking obtained from the ground truth collective efficacy
values. In our experiments, we find that our generated ranking of neighborhoods
achieves 0.77 Kendall tau-x ranking agreement with the ground truth ranking.
Overall, our results are up to 37% better than traditional baselines.
