This　paper　presents　RealTIS,　a　Real-time　emergent　Topic　Identification　System　for　user-generated　content　on　the　web　via　social　networking　services　such　as　Twitter,　Weibo,　and　Facebook.　Without　user　intervention,　our　proposed　RealTIS　system　can　efficiently　collect　necessary　social　media　posts,　construct　a　quality　topic　summarization　from　the　vast　sea　of　data,　and　then　automatically　identify　whether　the　emerging　topics　will　be　out-breaking　or　just　fading　into　silence.　RealTIS　uses　a　time-sliding　window　to　compute　the　statistics　about　the　basic　structure　(motifs)　variation　of　the　propagation　network　for　a　specific　topic.　These　statistics　are　then　used　to　predict　unusual　shifts　in　correlations,　make　early　warning　and　detect　outbreak.　Besides,　this　work　also　illustrates　the　mechanism　by　which　our　proposed　system　makes　early　warning　happen.　Copyright　©　2022,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.