In　recent　years,　the　security　of　online　social　networks　(OSNs)　has　become　an　issue　of　widespread　concern.　Searching　and　detecting　compromised　accounts　in　OSNs　is　crucial　for　ensuring　the　security　of　OSN　platforms.　In　this　study,　the　authors　proposed　a　new　method　of　detecting　compromised　accounts　based　on　a　supervised　analytical　hierarchy　process　(SAHP).　First,　they　considered　the　expression　habits　of　a　user　to　present　the　profile　features　of　a　user　more　comprehensively　than　previous　research.　Next,　the　information　gain　ratio　was　combined　with　the　analytical　hierarchy　process　algorithm　to　calculate　the　weight　of　each　feature.　Finally,　a　detection　decision　was　taken,　and　varying　thresholds　were　used　to　obtain　different　detection　results.　The　experimental　results　showed　that　the　accuracy　and　precision　of　the　SAHP　were　81.7　and　96.4%,　respectively.　The　results　indicated　that　the　new　method　improved　upon　the　previously　established　COMPA　(detecting　compromised　accounts　on　social　networks)　methods　for　detecting　compromised　accounts.