The last decade has witnessed of a rising surge interest in Gossip protocols in distributed systems. In particular, as soon as there is a need to disseminate events, they become a key functional building block due to their scalability, robustness and fault tolerance under high churn. However, Gossip protocols are known to be bandwidth intensive. A huge amount of algorithms has been studied to limit the number of exchanged messages using different combination of push/pull approaches. We are revisiting the state of the art by applying Random Linear Network Coding to further increase performances. In particular, the originality of our approach is to combine sparse vector encoding to send our network coding coefficients and Lamport timestamps to split messages in generations in order to provide an efficient gossiping. Our results demonstrate that we are able to drastically reduce the bandwidth overhead and the delay compared to the state of the art.