Recently,　the　rapid　diffusion　of　malicious　information　in　online　social　networks　causes　great　harm　to　our　society.　Therefore,　it　is　of　great　significance　to　localize　diffusion　sources　as　early　as　possible　to　stem　the　spread　of　malicious　information.　This　paper　proposes　a　novel　sensor-based　method,　called　greedy　full-order　neighbor　localization　(denoted　as　GFNL),　to　solve　this　problem　under　a　low　infection　propagation　in　line　with　the　real　world.　More　specifically,　GFNL　includes　two　main　components,　i.e.,　the　greedy-based　sensor　deployment　strategy　(DS)　and　direction-path-based　source　estimation　strategy　(ES).　In　more　detail,　to　ensure　sensors　can　observe　a　propagation　information　as　early　as　possible,　a　set　of　sensors　is　deployed　in　a　network　to　minimize　the　geodesic　distance　(i.e.,　the　distance　of　the　shortest　path)　between　the　candidate　set　and　the　sensor　set　based　on　DS.　Then　when　a　fraction　of　sensors　observe　a　propagation,　ES　infers　the　source　based　on　the　idea　that　the　distance　of　the　actual　propagation　path　is　proportional　to　the　observed　time.　Compared　with　some　state-of-the-art　methods,　comprehensive　experiments　have　proved　the　superiority　and　robustness　of　our　proposed　GFNL.