Malicious　social　robots,　which　are　disseminators　of　malicious　information　on　social　networks,　seriously　affect　information　security　and　network　environments.　The　detection　of　malicious　social　robots　is　a　hot　topic　and　a　significant　concern　for　researchers.　A　method　based　on　classification　has　been　widely　used　for　social　robot　detection.　However,　this　method　of　classification　is　limited　by　an　unbalanced　data　set　in　which　legitimate,　negative　samples　outnumber　malicious　robots　(positive　samples),　which　leads　to　unsatisfactory　detection　results.　This　paper　proposes　the　use　of　generative　adversarial　networks　(GANs)　to　extend　the　unbalanced　data　sets　before　training　classifiers　to　improve　the　detection　of　social　robots.　Five　popular　oversampling　algorithms　were　compared　in　the　experiments,　and　the　effects　of　imbalance　degree　and　the　expansion　ratio　of　the　original　data　on　oversampling　were　studied.　The　experimental　results　showed　that　the　proposed　method　achieved　better　detection　performance　compared　with　other　algorithms　in　terms　of　the　F1　measure.　The　GAN　method　also　performed　well　when　the　imbalance　degree　was　smaller　than　15%.