Despite the extensive research efforts in information diffusion, most previous studies focus on the speed and coverage of the diffused information in the network. A better understanding on the semantics of information diffusion can provide critical information for the domain-specific/socio-economic phenomenon studies based on diffused topics. More specifically, it still lacks (a) a comprehensive understanding of the multiplexity in the diffused topics, especially with respect to the temporal relations and inter-dependence between topic semantics; (b) the similarities and differences in these dimensions under different diffusion degrees. In this paper, the semantics of a topic is described by sentiment, controversy, content richness, hotness, and trend momentum. The multiplexity in the diffusion mechanisms is also considered, namely, hashtag cascade, url cascade, and retweet. Our study is conducted upon 840, 362 topics from about 42 million tweets during 2010.01–2010.10. The results show that the topics are not randomly distributed in the Twitter space, but exhibiting a unique pattern at each diffusion degree, with a significant correlation among content richness, hotness, and trend momentum. Moreover, under each diffusion mechanism, we also find the remarkable similarity among topics, especially when considering the shifting and scaling in both the temporal and amplitude scales of these dimensions.