Topical　communities　have　shown　useful　tools　for　characterizing　social　networks.　However　data　in　social　networks　often　come　as　streams,　i.e.,　both　text　content　(e.g.,　emails,　user　postings)　and　network　structure　(e.g.,　user　friendship)　evolve　over　time.　We　propose　two　nonparametric　statistic　models　where　infinite　latent　community　variables　coupled　with　infinite　latent　topic　variables.　The　temporal　dependencies　between　variables　across　epochs　are　modeled　via　a　rich-gets-richer　scheme.　We　focus　on　characterizing　three　dynamic　aspects　in　social　streams:　the　number　of　communities　or　topics　changes　(e.g.,　new　communities　or　topics　are　born　and　old　ones　die　out);　the　popularity　of　communities　or　topics　evolves;　the　semantics　such　as　community　topic　distribution,　community　participant　distribution　and　topic　word　distribution　drift.　Furthermore,　we　develop　an　effective　online　posterior　inference　algorithm　for　the　models,　which　is　concordant　with　the　online　nature　of　social　streams.　Experiments　using　real-world　data　show　the　effectiveness　of　our　model　at　discovering　the　dynamic　topical　communities　in　social　streams.　(C)　2016　Elsevier　B.V.　All　rights　reserved.