Online　social　networks　have　played　an　important　role　in　people＇s　common　life.　Most　existing　social　network　platforms,　however,　face　the　challenges　of　dealing　with　undesirable　users　and　their　malicious　spam　activities　that　disseminate　content,　malware,　viruses,　etc.　to　the　legitimate　users　of　the　service.　In　this　paper,　an　Extreme　Learning　Machine　based　supervised　machine　is　proposed　for　effective　spammer　detection.　The　experiment　and　evaluation　show　that　the　proposed　solution　provides　excellent　performance　with　a　true　positive　rate　of　spammers　and　non-spammers　reaching　99%　and　99.95%,　respectively.　As　the　results　suggest,　the　proposed　solution　could　achieve　better　reliability　and　feasibility　compared　with　existing　SVM　based　approaches.