Based　on　the　supernetwork　theory,　a　two-step　rumor　detection　model　was　proposed.　The　first　step　was　the　classification　of　users　on　the　basis　of　user-based　features.　In　the　second　step,　non-user-based　features,　including　psychology-based　features,　content-based　features,　and　parts　of　supernetwork-based　features,　were　used　to　detect　rumors　posted　by　different　types　of　users.　Four　machine　learning　methods,　namely,　Naive　Bayes,　Neural　Network,　Support　Vector　Machine,　and　Logistic　Regression,　were　applied　to　train　the　classifier.　Four　real　cases　and　several　assessment　metrics　were　employed　to　verify　the　effectiveness　of　the　proposed　model.　Performance　of　the　model　regarding　early　rumor　detection　was　also　evaluated　by　separating　the　datasets　according　to　the　posting　time　of　posts.　Results　showed　that　this　model　exhibited　better　performance　in　rumor　detection　compared　to　five　benchmark　models,　mainly　owing　to　the　application　of　the　supernetwork　theory　and　the　two-step　mechanism.