Online　social　networks,　such　as　Facebook,　Twitter,　and　Weibo　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.　The　spreading　of　spam　degrades　user　experience　and　also　negatively　impacts　server-side　functions　such　as　data　mining,　user　behavior　analysis,　and　resource　recommendation.　In　this　paper,　an　extreme　learning　machine　(ELM)-based　supervised　machine　is　proposed　for　effective　spammer　detection.　The　work　first　constructs　the　labeled　dataset　through　crawling　Sina　Weibo　data　and　manually　classifying　corresponding　users　into　spammer　and　non-spammer　categories.　A　set　of　features　is　then　extracted　from　message　content　and　user　behavior　and　applies　them　to　the　ELM-based　spammer　classification　algorithm.　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.