With the rapid proliferation of microblogging services such as Twitter, a large number of tweets is published everyday often making users feel overwhelmed with information. Helping these users to discover potentially interesting tweets is an important task for such services. In this paper, we present a novel tweet-recommendation approach, which exploits network, content, and retweet analyses for making recommendations of tweets. The idea is to recommend tweets that are not visible to the user (i.e., they do not appear in the user timeline) because nobody in her social circles published or retweeted them. To do that, we create the user's ego-network up to depth two and apply the transitivity property of the friends-of-friends relationship to determine interesting recommendations, which are then ranked to best match the user's interests. Experimental results demonstrate that our approach improves the state-of-the-art technique.