Natural disasters and disruptive events were a major reason for researchers to study networks and community vulnerability. Detecting communities is considered a key element to better understand social networks. This detection will allow researchers to discover community structures inside the network and apply several methods to determine influencers inside each community, which in terms will help in evaluating community vulnerability. In this study, Girvan Newman community detection algorithm is applied to detect communities in social networks. This algorithm detects communities based on their betweenness centrality. Several methods have been established to study the spread of influence in social networks such as the Linear Threshold model. Understanding the spread of influence inside communities will help in categorizing community vulnerability. After detecting communities, an influence optimization method using Linear Threshold will be applied to help identifying optimal influencers in each community. The proportion of influencers in each community will be the indicator of social vulnerability. The higher the proportion of influencers in the community, the more resilient the community will be in terms of spreading information inside the network. Sensitivity analysis will be implemented to evaluate the behavior of each community when changes are made to thresholds and the number of initial influencers. The main goal of this study is to identify vulnerable communities and prioritize them, which can help in preparedness for any disruptive event such as natural disasters.