With　the　popularity　of　social　networks,　social　bots　are　increasingly　interfering　with　human　social　activities,　which　have　negatively　impacted　social　network　ecology　and　user　experience.　How　to　detect　social　bots　effectively　has　become　a　challenging　task.　Most　of　existing　social　bot　detection　methods　are　based　on　the　supervised　learning　mechanism,　but　the　labelled　data　collection　is　resource-intensive.　In　this　paper,　a　Multi-stage　Self-training　Social　Bot　detection　method　based　on　a　Graph　Neural　Network　(MSSBot)　is　proposed　to　improve　the　performance　of　social　bot　detection　with　a　small　amount　of　labelled　data.　MSSBot　exploits　pseudo-labels　to　expand　the　dataset　and　trains　a　Relational　Graph　Convolutional　Network　in　multiple　stages,　while　uncertainty-aware　and　deep　clustering　are　used　to　reduce　the　noise　present　in　the　pseudo-labels　acquisition　process.　The　experiments　were　conducted　on　a　real-world　social　network　dataset　Twibot-20,　a　small　high-quality　dataset　focused　on　social　bot　behavior　patterns　Cresci-2015,　and　a　large-scale　graph-based　dataset　Twibot-22　with　high　annotation　quality.　The　experimental　results　show　that　our　method　achieves　accuracies　of　0.9259,　0.9979,　and　0.8358　on　the　Twibot-20,　Cresci-2015,　and　Twibot-22　datasets,　respectively,　outperforming　the　state-of-the-art　methods,　with　improvements　of　5.68　%,　2.64　%,　and　3.92　%　over　the　existing　state-of-the-art　methods.　©　2025　Elsevier　Ltd