With　the　introduction　of　many　image　compression　standards,　the　social　images　are　stored　and　transmitted　in　compressed　formats　such　as　JPEG.　For　large-scale　image　database,　tag　ranking　must　fully　decompress　the　compressed　data　to　predict　tag　relevance　based　on　visual　content.　In　order　to　improve　the　accuracy　of　tag　ranking　and　further　reduce　the　ranking　time,　social　images　tag　ranking　based　on　visual　words　in　compressed　domain　is　proposed　in　this　paper,　which　includes　three　steps:　(1)　low-resolution　social　images　are　constructed　from　the　compressed　image　data;　(2)　visual　words　are　created　according　to　extracted　SIFT　descriptors　in　low-resolution　social　image;　(3)　the　neighbor　voting　model　is　utilized　to　rank　the　image　tags　after　matching　the　similarity　based　on　visual　words　of　an　image.　In　order　to　evaluate　the　performance　of　the　proposed　method,　average　NDCG　(normalized　discounted　cumulative　gain)　and　tag　ranking　time　are　compared.　Experimental　results　show　that　the　proposed　method　can　significantly　reduce　the　time　of　image　tag　ranking　under　ensuring　the　ranking　accuracy　of　social　image　tags.　(C)　2014　Elsevier　B.V.　All　rights　reserved.