In　web　topic　detection,　detecting　＇hot＇　topics　from　enormous　User-Generated　Content　(UGC)　on　web　data　poses　two　main　difficulties　that　conventional　approaches　can　barely　handle:　1)　poor　feature　representations　from　noisy　images　and　short　texts;　and　2)　uncertain　roles　of　modalities　where　visual　content　is　either　highly　or　weakly　relevant　to　textual　cues　due　to　less-constrained　data.　In　this　paper,　following　the　detection　by　ranking　approach,　we　address　the　problem　by　learning　a　robust　shared　representation　from　multiple,　noisy　and　complementary　features,　and　integrating　both　textual　and　visual　graphs　into　a　k-Nearest　Neighbor　Similarity　Graph　(k-N2SG).　Then　Non-negative　Matrix　Factorization　using　Random　walk　(NMFR)　is　introduced　to　generate　topic　candidates.　An　efficient　fusion　of　multiple　graphs　is　then　done　by　a　Latent　Poisson　Deconvolution　(LPD)　which　consists　of　a　poisson　deconvolution　with　sparse　basis　similarities　for　each　edge.　Experiments　show　significantly　improved　accuracy　of　the　proposed　approach　in　comparison　with　the　state-of-the-art　methods　on　two　public　data　sets.　©　2016　IEEE.