Organizing　webpages　into　interesting　topics　is　one　of　the　key　steps　to　understand　the　trends　from　multimodal　Web　data.　The　sparse,　noisy,　and　less-constrained　user-generated　content　results　in　inefficient　feature　representations.　These　descriptors　unavoidably　cause　that　a　detected　topic　still　contains　a　certain　number　of　the　false　detected　webpages,　which　further　make　a　topic　be　less　coherent,　less　interpretable,　and　less　useful.　In　this　paper,　we　address　this　problem　from　a　viewpoint　interpreting　a　topic　by　its　prototypes,　and　present　a　two-step　approach　to　achieve　this　goal.　Following　the　detection-by-ranking　approach,　a　sparse　Poisson　deconvolution　is　proposed　to　learn　the　intratopic　similarities　between　webpages.　To　find　the　prototypes,　leveraging　the　intratopic　similarities,　top-k　diverse　yet　representative　prototype　webpages　are　identified　from　a　submodularity　function.　Experimental　results　not　only　show　the　improved　accuracies　for　the　Web　topic　detection　task,　but　also　increase　the　interpretation　of　a　topic　by　its　prototypes　on　two　public　datasets.