Analyzing the lifecycle of topics, that are present in user-generated text content, has emerged as a mainstream topic of social network research. The literature presently identifies topics on Twitter, a prominent online social network, as either individual hashtags, or a burst of keywords within a short span of time, or as latent concept spaces obtained from sophisticated text analysis mechanisms, such as Latent Dirichlet Allocation (LDA). The first and second approaches fail to recognize that topics do not restrict themselves to individual hashtags and are likely to span across (semantically related) keywords, while the third does not capture the user’s intended topics expressed via hashtags. In the current paper, we propose a novel methodology that addresses these shortcomings. We jointly utilize the temporal concurrency of the hashtags contained in given tweets and the latent concept space addressed by the tweet content, to identify groups of hashtags representing concept space—a “topic”—addressed by many tweets. A given topic, thus, is represented by a different set of representative hashtags at different times; the usage rate of the different hashtags change such that some hashtags gain prominence over others over time. Unlike the literature, where lifecycle analysis of one topic typically comprises of analyzing one hashtag, we analyze and characterize the lifecycle of a topic as a combination of multiple semantically and temporally related hashtags. We derive novel insights about lifecyle of topics: the inception and continuity of the topics over time (expressed over different hashtags), and how topics morph over hashtags, from one set of hashtags to another, before eventually dying down. © Springer International Publishing AG 2018.