With the rapid development of the Internet and its applications, growing volumes of documents increasingly become interconnected to form large‐scale document networks. Accordingly, topic modeling in a network of documents has been attracting continuous research attention. Most of the existing network‐based topic models assume that topics in a document are influenced by its directly linked neighbouring documents in a document network and overlook the potential influence from indirectly linked ones. The existing work also has not carefully modeled variations of such influence among neighboring documents. Recognizing these modeling limitations, this paper introduces a novel Local Context‐Aware LDA Model (LC‐LDA), which is capable of observing a local context comprising a rich collection of documents that may directly or indirectly influence the topic distributions of a target document. The proposed model can also differentiate the respective influence of each document in the local context on the target document according to both structural and temporal relationships between the two documents. The proposed model is extensively evaluated through multiple document clustering and classification tasks conducted over several large‐scale document sets. Evaluation results clearly and consistently demonstrate the effectiveness and superiority of the new model with respect to several state‐of‐the‐art peer models.