This paper analyzes how social structure and social reinforcement affect the diffusion of an idea in a population of human agents. A percolation approach is used to model the diffusion process. This framework assumes that information is local and embedded in a social network. We introduce social reinforcement in the model by softening the condition to adopt when the number of adopting neighbors increases. Our numerical analysis shows that social reinforcement severely affects the output of the process. Some ideas with an original value so low that it would not get diffused through percolation can be spread due to the strength of social reinforcement. This effect also interacts with the structure of the network, getting a more sizeable impact on small worlds with a low rewiring probability. Also, social reinforcement completely changes the effect of clustering links, because sequential adoption of neighbors can make one agent adopt at later stages.