Semantic　concepts　selection　for　model　construction　and　data　collection　is　an　open　research　question.　It　is　highly　demanding　to　choose　good　multimedia　concepts　with　small　semantic　gaps　to　facilitate　the　work　of　cross-media　system　developers.　Since,　this　work　is　very　scarce　therefore;　this　paper　contributes　a　new　real-world　web　image　dataset　created　by　NGN　Tsinghua　Laboratory　students　for　cross　media　search.　Unlike　previous　datasets,　such　as　Flicker30k,　Wikipedia　and　NUS　have　high　semantic　gap,　results　in　leading　to　inconsistency　with　real　time　applications.　To　overcome　these　drawbacks,　the　proposed　Facebook5k　dataset　includes:　(1)　5130　images　crawled　from　Facebook　through　users　feelings;　(2)　Images　are　categorized　according　to　users　feelings;　(3)　Facebook5k　is　independent　of　tags　and　language,　rather　than　uses　feelings　for　search.　Based　on　the　proposed　dataset,　we　point　out　key　features　of　social　website　images　and　identify　some　research　problems　on　image　annotation　and　retrieval.　The　benchmark　results　show　the　effectiveness　of　the　proposed　dataset　to　simplify　and　improve　general　image　retrieval.　©　Springer　Nature　Switzerland　AG　2018.