Being able to recommend links between users in online social networks is important for users to connect with like-minded individuals as well as for the platforms themselves and third parties leveraging social media information to grow their business. Predictions are typically based on unsupervised or supervised learning, often leveraging simple yet effective graph topological information, such as the number of common neighbors. However, we argue that richer information about personal social structure of individuals might lead to better predictions. In this article, we propose to leverage well-established social cognitive theories to improve link prediction performance. According to these theories, individuals arrange their social relationships along, on average, five concentric circles of decreasing intimacy. We postulate that relationships in different circles have different importance in predicting new links. To validate this claim, we focus on popular feature extraction prediction algorithms (both unsupervised and supervised) and we extend them to include social circles’ awareness. We validate the prediction performance of these circle-aware algorithms against several benchmarks (including their baseline versions as well as node-embedding- and graph neural network (GNN)-based link prediction), leveraging two Twitter datasets comprising a community of video gamers and generic users. We show that social awareness generally provides significant improvements in prediction performance, beating also state-of-the-art solutions such as node2vec and learning from Subgraphs, Embeddings and Attributes for Link prediction (SEAL), and without increasing the computational complexity. Finally, we show that social awareness can be used in place of using a classifier (which may be costly or impractical) for targeting a specific category of users.