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Sociologists associate the spatial variation of crime within an urbansetting, with the concept of collective efficacy. The collective efficacy of aneighborhood is defined as social cohesion among neighbors combined with theirwillingness to intervene on behalf of the common good. Sociologists measurecollective efficacy by conducting survey studies designed to measureindividuals' perception of their community. In this work, we employ the curateddata from a survey study (ground truth) and examine the effectiveness ofsubstituting costly survey questionnaires with proxies derived from socialmedia. We enrich a corpus of tweets mentioning a local venue with severallinguistic and topological features. We then propose a pairwise learning torank model with the goal of identifying a ranking of neighborhoods that issimilar to the ranking obtained from the ground truth collective efficacyvalues. In our experiments, we find that our generated ranking of neighborhoodsachieves 0.77 Kendall tau-x ranking agreement with the ground truth ranking.Overall, our results are up to 37% better than traditional baselines.

Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods