The　optimization　of　influence　on　social　networks　has　received　significant　attention,　yet　there　has　been　limited　research　on　blocking　rumors　targeted　at　specific　individuals.　In　this　paper,　we　introduce　the　Rumor　Blocking　for　Target　Users　(RBTU)　problem,　which　aims　to　identify　a　set　of　influential　seed　nodes　to　minimize　the　number　of　target　users　affected　by　the　rumor.　To　address　scalability　challenges,　we　propose　a　two-stage　distributed　algorithm　that　utilizes　community　partitioning　and　reverse　sampling　techniques.　In　the　first　stage,　the　original　network　is　partitioned　into　non-overlapping　communities.　This　division　serves　to　reduce　the　complexity　of　the　problem　and　allows　for　parallel　processing.　In　the　second　stage,　reverse　sampling　is　performed　independently　for　each　community.　This　process　involves　the　strategic　selection　of　seed　nodes　to　restrict　the　spread　of　the　rumor.　Additionally,　we　provide　both　theoretical　and　empirical　analyses　of　our　proposed　algorithm.　Theoretically,　we　prove　that　the　objective　function　is　submodular,　which　enables　the　greedy　algorithm　to　achieve　an　approximate　ratio　of　(1-1/e).　Through　numerical　simulations,　we　demonstrate　that　our　algorithm　outperforms　other　comparison　methods.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.