With　the　rapid　development　of　the　Internet　since　the　beginning　of　the　21st　century,　social　networks　have　provided　a　significant　amount　of　convenience　for　work,　study,　and　entertainment.　Specifically,　because　of　the　irreplaceable　superiority　of　social　platforms　in　disseminating　information,　criminals　have　thus　updated　the　main　methods　of　social　engineering　attacks.　Detecting　abnormal　accounts　on　social　networks　in　a　timely　manner　can　effectively　prevent　the　occurrence　of　malicious　Internet　events.　Different　from　previous　research　work,　in　this　work,　a　method　of　anomaly　detection　called　Hurst　of　Interest　Distribution　is　proposed　based　on　the　stability　of　user　interest　quantifiable　from　the　content　of　users＇　tweets,　so　as　to　detect　abnormal　accounts.　In　detail,　the　Latent　Dirichlet　Allocation　model　is　adopted　to　classify　blog　content　on　Twitter　into　topics　to　calculate　and　obtain　the　topic　distribution　of　tweets　sent　by　a　single　user　within　a　period　of　time.　Then,　the　stability　degree　of　the　user＇s　tweet　topic　preference　is　calculated　according　to　the　Hurst　index　to　determine　whether　the　account　is　compromised.　Through　experiments,　the　Hurst　indexes　of　normal　and　abnormal　accounts　are　found　to　be　significantly　different,　and　the　detection　rate　of　abnormal　accounts　using　the　proposed　method　can　reach　up　to　97.93%.