Microblogging　filtering　is　intended　to　filter　out　irrelevant　content,　and　select　useful,　new,　and　timely　content　from　microblogs.　However,　microblogging　filtering　suffers　from　the　problem　of　insufficient　samples　which　renders　the　probabilistic　models　unreliable.　To　mitiiate　this　problem,　a　novel　method　is　proposed　in　this　study.　It　is　believed　that　an　explicit　brief　query　is　only　an　abstract　of　the　user＇s　information　needs,　and　it＇s　difficult　to　infer　users＇　actual　searching　intents　and　interests.　Based　on　this　belief,　a　filtering　model　is　built　where　the　multi-sources　query　expansion　in　microblogging　filtering　is　exploited　and　expanded　query　is　submitted　as　user＇s　interest.　To　manage　the　external　expansion　risk,　a　user　filter　graph　inference　method　is　proposed,　which　is　characterized　by　combination　of　external　multi-sources　information,　and　a　risk　minimization　filtering　model　is　introduced　to　achieve　the　best　reasoning　through　the　multi-sources　expansion.　A　series　of　experiments　are　conducted　to　evaluate　the　effectiveness　of　proposed　framework　on　an　annotated　tweets　corpus.　The　results　of　these　experiments　show　that　our　method　is　effective　in　tweets　retrieval　as　compared　with　the　baseline　standards.