Social interactions preceding and succeeding trust formation can be significant indicators of formation of trust in online social networks. In this research we analyze the social interaction trends that lead and follow formation of trust in these networks. This enables us to hypothesize novel theories responsible for explaining formation of trust in online social settings and provide key insights. We find that a certain level of socialization threshold needs to be met in order for trust to develop between two individuals. This threshold differs across persons and across networks. Once the trust relation has developed between a pair of characters connected by some social relation (also referred to as a character dyad), trust can be maintained with a lower rate of socialization. Our first set of experiments is the relationship prediction problem. We predict the emergence of a social relationship like grouping, mentoring and trading between two individuals over a period of time by looking at the past characteristics of the network. We find that features related to trust have very little impact on this prediction. In the final set of experiments, we predict the formation of trust between individuals by looking at the topographical and semantic social interaction features between them. We generate three semantic dimensions for this task which can be recomputed with an observed social variable (say grouping) to create a new semantic social variable. In this endeavor, we successfully show that, including features related to socialization, gives us an approximate increase of 4-9% accuracy for trust relationship predictions.