Organizing　webpages　into　hot　topics　is　one　of　the　key　steps　to　understand　the　trends　from　multi-modal　web　data.　To　handle　this　pressing　problem,　Poisson　Deconvolution　(PD),　a　state-of-the-art　method,　recently　is　proposed　to　rank　the　interestingness　of　web　topics　on　a　similarity　graph.　Nevertheless,　in　terms　of　scalability,　PD　optimized　by　expectation-maximization　is　not　sufficiently　efficient　for　a　large-scale　data　set.　In　this　paper,　we　develop　a　Stochastic　Poisson　Deconvolution　(SPD)　to　deal　with　the　large-scale　web　data　sets.　Experiments　demonstrate　the　efficacy　of　the　proposed　approach　in　comparison　with　the　state-of-the-art　methods　on　two　public　data　sets　and　one　large-scale　synthetic　data　set.　©　2019,　Springer　Nature　Switzerland　AG.