In today's digital landscape, online social networks have become pivotal platforms for communication, content sharing, and information seeking. However, the huge amount of content has led to information overload, posing a significant challenge for users. This paper introduces a novel recommender system designed to mitigate this problem by integrating the concept of social capital into the recommendation process. Social capital, encompassing trust, influence, and relationships within social networks, is leveraged to enhance the relevance and personalization of news recommendations. By analyzing news with high user interactions and influence propagation, the system ensures that users receive content aligned with their preferences. The proposed methodology is detailed, and experimental evaluation demonstrates its effectiveness.