Word-of-mouth communication has been shown to play a key role in a variety of environments such as viral marketing and virus spreading. A family of algorithms, generally known as information spreading algorithms or word-of-mouth algorithms, has been developed to characterize such behavior. These algorithms are able to model important aspects of this type of communication. However, they have limitations, including the inability to: (1) capture when the communications or contacts take place and (2) explain where the influence comes from. These drawbacks have limited the studies about how the spreading of influence takes place in social networks. In this paper, we present a new word-ofmouth algorithm that considers the temporality of the communications and keeps track of how influence travels over the social network. We validate the proposed algorithm via simulations of word-of-mouth traces on call detailed records, in order to model how influence spreads. Our results indicate that (1) s...