A social network, such as Facebook or Twitter, is a collection of social actors—like individuals or organizations—connected through relationships that represent social interactions. It describes a dynamic social structure made up of nodes and edges. The ability to analyze and identify communities within the network can aid in understanding and visualizing the structure of these networks. Social entities interact in various ways, forming groups, sending messages, sharing articles, joining discussion groups, and so on. Numerous approaches have been proposed to discover community structures within networks. While some methods have produced acceptable results, no algorithm has yet been able to deliver completely accurate outcomes. In this work, we present a novel approach to community detection in social networks. Our method is based on the Edge-Betweenness algorithm, which identifies communities by analyzing the exchange of information between nodes about their community memberships. We implemented an evaluation of our approach and compared the results with those found in the literature. The results obtained are considered satisfactory and demonstrate the potential of our method in accurately identifying community structures in social networks.