Detecting　＂hot＂　topics　from　the　enormous　user-generated　content　(UGC)　data　on　web　poses　two　main　difficulties　that　the　conventional　approaches　can　barely　handle:　1)　poor　feature　representations　from　noisy　images　or　short　texts,　and　2)　uncertain　roles　of　modalities　where　the　visual　content　is　either　highly　or　weakly　relevant　to　the　textual　cues　due　to　the　less-constrained　UGC.　In　this　paper,　following　the　detection-by-ranking　approach,　we　address　above　challenges　by　learning　a　robust　latent　representation　from　multiple,　noisy　and　a　high　probability　of　the　complementary　features.　Both　the　textual　features　and　the　visual　ones　are　encoded　into　a　k-nearest　neighbor　hybrid　similarity　graph　(HSG),　where　nonnegative　matrix　factorization　using　random　walk　is　introduced　to　generate　topic　candidates.　An　efficient　fusion　of　multiple　HSGs　is　then　done　by　a　latent　poisson　deconvolution,　which　consists　of　a　poisson　deconvolution　with　sparse　basis　similarity　for　each　edge.　Experiments　show　significantly　improved　accuracy　of　the　proposed　approach　in　comparison　with　the　state-of-the-art　methods　on　two　public　datasets.