Community detection in temporal social networks is an increasingly challenging subject in network analysis. There exists aA variety of approaches exist in detecting communities in dynamic social networks among which the label propagation approach is outstanding. However, in LPA, new nodes cannot compete with old nodes in order to form new communities and they tend to join to existing communities; therefore, it increases the probability of generating monster communities, especially in dynamic social networks. This drawback decreases the accuracy of community detection in these networks. Spreading activation is a searching process used in semantic networks added to the Speaker Listener Propagation Algorithm (LPA) in order to solve this drawback. Besides, the present write examines three weighting algorithms in order to weight edges of the graph. Three weighting algorithms leads to separate variations of the proposed method. Here, the newly proposed method, - Speaker Listener Propagation Algorithm Dynamic (SLPAD), DLPAE-Overlapping on real and synthetic networks are implemented. Theis finding here, indicates that all variations of the proposed method detects communityies more accurately over time compared to the benchmark methods.