With social media platform such as Twitter becoming the de facto destination for users' views and opinions, it is of great importance to forecast an information outbreak. In Twitter, tweets are often annotated with hashtags to help its users to quickly extract their contents. The existing approaches for modeling the dynamics of tweet-messages are usually limited to individual or simple aggregates of tweets rather than the underlying hashtags. In this paper, we develop, STRM, a novel point process driven model that considers the effect of cross-tweet impact in hashtag popularity. STRM, by assuming hashtag to be a heterogeneous collection of tweet-chains. Through extensive experimentation, we find that our algorithm — STRM, shows consistent performance boosts with six diverse real datasets against several strong baselines. Moreover surprisingly, it also offers significant accuracy gains in popularity-prediction for individual tweets as compared with the existing paradigms.