Microblogging　filtering　can　help　users　filter　out　irrelevant　content,　and　extract　timely　content　effectively　from　microblogs.　However,　as　a　typical　short　text,　microblogging　filtering　suffers　from　the　insufficient　samples　problem　that　makes　the　probabilisticlike　models　unreliable.　According　to　the　current　research,　an　explicit　brief　query　has　been　thought　to　be　only　an　abstract　of　the　user＇s　information　needs,　and　it＇s　hard　to　infer　what　is　the　users＇　actual　searching　intents.　Instead,　we　submit　the　relevant　external　documents　as　a　user＇s　implicit　prior　knowledge　and　then　build　a　corresponding　filtering　framework.　To　against　the　risk　of　external　documents　expansion,　we　suppose　the　external　document　can　be　viewed　as　a　complete　statement　of　an　explicit　query,　and　encode　the　filtering　preferences　with　the　diverge　degree　between　the　external　document　and　the　the　original　explicit　query.　Thus　the　optimal　filtering　action　is　the　one　that　allows　one　to　trade　off　diverge　degree　against　generalization　performance.　With　respect　to　the　established　baselines,　our　algorithm　yields　compelling　results　for　providing　a　meaningful　tweets　retrieval.　This　work　helps　further　understand　the　innate　risk　characteristics　of　external　expansion　for　the　design　of　Microblogging　filtering　systems.