Although　topic　detection　and　tracking　techniques　have　made　great　progress,　most　of　the　researchers　seldom　pay　more　attention　to　the　following　two　aspects.　First,　the　construction　of　a　topic　model　does　not　take　the　characteristics　of　different　topics　into　consideration.　Second,　the　factors　that　determine　the　formation　and　development　of　hot　topics　are　not　further　analyzed.　In　order　to　correctly　extract　news　blog　hot　topics,　the　paper　views　the　above　problems　in　a　new　perspective　based　on　the　W2T　(Wisdom　Web　of　Things)　methodology,　in　which　the　characteristics　of　blog　users,　context　of　topic　propagation　and　information　granularity　are　investigated　in　a　unified　way.　The　motivations　and　features　of　blog　users　are　first　analyzed　to　understand　the　characteristics　of　news　blog　topics.　Then　the　context　of　topic　propagation　is　decomposed　into　the　blog　community,　topic　network　and　opinion　network,　respectively.　Some　important　factors　such　as　the　user　behavior　pattern,　opinion　leader　and　network　opinion　are　identified　to　track　the　development　trends　of　news　blog　topics.　Moreover,　a　blog　hot　topic　detection　algorithm　is　proposed,　in　which　news　blog　hot　topics　are　identified　by　measuring　the　duration,　topic　novelty,　attention　degree　of　users　and　topic　growth.　Experimental　results　show　that　the　proposed　method　is　feasible　and　effective.　These　results　are　also　useful　for　further　studying　the　formation　mechanism　of　opinion　leaders　in　blogspace.