Events　are　real-world　occurrences　that　lead　to　the　explosive　growth　of　web　multimedia　content　such　as　images,　videos　and　texts.　Efficient　organization　and　navigation　of　multimedia　data　in　the　topic　level　can　boost　users＇　understanding　and　enhance　their　experience　of　the　events　that　have　happened.　Due　to　the　potential　application　prospects,　multimedia　topic　detection　has　been　an　active　area　of　research　with　notable　progress　in　the　last　decade.　Traditional　methods　mainly　focus　on　single　media,　so　the　results　only　reflect　the　characteristics　of　one　certain　media　and　topic　browsing　was　not　comprehensive　enough.　In　this　paper,　we　propose　a　method　of　utilizing　and　fusing　rich　media　information　from　web　videos　and　news　reports　to　extract　weighted　keyword　groups,　which　are　used　for　cross-media　topic　detection.　Firstly　by　utilizing　the　video-related　textual　information　and　the　titles　of　news　articles,　a　maximum　local　average　score　is　proposed　to　find　coarse　weighted　dense　keyword　groups;　after　that,　textual　linking　and　visual　linking　are　applied　to　refine　the　keyword　groups　and　update　the　weights;　finally,　the　documents　are　re-linked　with　the　refined　keyword　groups　to　form　an　event-related　document　set.　Experiments　are　conducted　on　cross-media　datasets　containing　web　videos　and　news　reports.　The　web　videos　are　from　Youku,　YouTube＇s　equivalent　in　China,　the　news　reports　from　sina.com,　some　of　which　contain　topic-related　images.　The　experimental　results　demonstrate　the　effectiveness　and　efficiency　of　the　proposed　approach.　(C)　2015　Elsevier　B.V.　All　rights　reserved.