Mobile　opportunistic　networks　take　advantage　of　contact　opportunities　due　to　the　mobility　of　individual　nodes　for　distributing　data.　Studying　the　inherent　traits　of　data　dissemination　in　mobile　opportunistic　networks　can　reveal　their　potential　to　support　emerging　applications,　such　as　emergency　services　and　mobile　commerce.　In　this　paper,　we　employ　the　Levy　mobility　model　to　characterize　the　movement　pattern　of　nodes.　Because　the　Levy　mobility　closely　mimics　human　walking　patterns,　the　analysis　model　that　we　adopt　is　realistic.　Our　analyses　consider　small-and　large-scale　perspectives.　In　the　small-scale　case,　we　investigate　the　distribution　of　the　minimum　time　needed　by　the　data　to　spread　to　a　given　region.　In　the　large-scale　case,　we　examine　the　probability　bounds　of　the　earliest　time　at　which　the　data　arrives　in　a　region　that　is　sufficiently　far　away.　We　also　derive　the　rate　at　which　such　a　probability　tends　to　zero　as　the　distance　to　the　region　increases　to　infinity.　The　results　of　extensive　numerical　simulations　validate　our　analysis.