In this paper we present encouraging preliminary results into the problem of social causality (causal reasoning used by intelligent agents in a social environment) in online social interactions based on a model of reciprocity. At every level, social relationships are guided by the shared understanding that most actions call for appropriate reactions, and that inappropriate reactions require management. Thus, we present an analysis of interpersonal relationships in English reciprocal contexts. Specifically, we rely here on a large and recently built database of 10,882 reciprocal relation instances in online media. The resource is analyzed along a set of novel and important dimensions: symmetry, affective value, gender}, and {\em intentionality of action which are highly interconnected. At a larger level, we automatically generate {\em chains of causal relations} between verbs indicating interpersonal relationships. Statistics along these dimensions give insights into people's behavior, judgments, and thus their social interactions.