Organizing　a　few　webpages　from　social　media　into　hot　topics　is　one　of　the　key　steps　to　understand　trends　on　web.　Discovering　popular　yet　hot　topics　from　web　faces　a　sea　of　noise　webpages　which　never　evolve　into　popular　topics.　In　this　paper,　we　discover　that　the　similarity　values　between　webpages　in　a　popular　topic　contain　the　statistically　similar　features　observed　in　L　&　eacute;vy　walks.　Consequently,　we　present　a　simple,　novel,　yet　very　powerful　Explore-Exploit　(EE)　approach　to　group　topics　by　simulating　L　&　eacute;vy　walks　nature　in　the　similarity　space.　The　proposed　EE-based　topic　clustering　is　an　effective　and　efficient　method　which　is　a　solid　move　towards　handling　a　sea　of　noise　webpages.　Experiments　on　two　public　data　sets　demonstrate　that　our　approach　is　not　only　comparable　to　the　State-Of-The-Art　(SOTA)　methods　in　terms　of　effectiveness　but　also　significantly　outperforms　the　SOTA　methods　in　terms　of　efficiency.