With　the　rapid　development　of　social　media,　research　about　its　application　and　organization　has　attracted　considerable　attention.　Many　social　media　sharing　websites　allow　users　to　tag　the　uploaded　images　with　tags.　However,　the　tags　are　usually　uncontrolled,　ambiguous,　and　overly　personalized.　One　of　the　most　challenging　issues　is　how　to　match　the　textual　tags　with　the　visual　image　to　rank　tags　accurately.　Because　a　great　deal　of　social　images　are　stored　and　transmitted　in　a　compressed　format,　an　optimized　tag　ranking　based　on　visual　vocabulary　in　compressed　domain　is　proposed　in　order　to　improve　the　accuracy　of　tag　ranking　and　further　reduce　the　ranking　time.　First,　the　low-resolution　social　images　are　constructed　from　the　compressed　image　data.　Then　a　visual　vocabulary　is　created　after　the　SIFT　descriptor　is　extracted.　Finally,　the　neighbor　voting　model　is　utilized　to　rank　the　image　tags.　Experimental　results　show　that　the　proposed　optimization　method　can　significantly　reduce　the　time　of　image　tag　ranking　under　ensuring　the　ranking　accuracy　of　social　image　tags.