Bot Detection on Cresci Standard 2015 (test)
97.2Accuracy / F1 ScoreDataset-Level*
Evaluation Results
| Method | Links | |
|---|---|---|
| Dataset-Level*Backbone Model=BotRGCN, Feature Category=Emotions2025.11 | 97.2 | |
| Text-Level*Backbone Model=BotRGCN, Feature Category=Emotions2025.11 | 96.9 | |
| Shortcut_trBackbone Model=BotRGCN, Feature Category=Topics2025.11 | 96.8 | |
| Text-Level*Backbone Model=BotRGCN, Feature Category=Topics2025.11 | 96.5 | |
| Model-Level*Backbone Model=BotRGCN, Feature Category=Emotions2025.11 | 95.9 | |
| Dataset-Level*Backbone Model=BotRGCN, Feature Category=Topics2025.11 | 95.8 | |
| Shortcut_trBackbone Model=BotRGCN, Feature Category=Emotions2025.11 | 94.1 | |
| Text-Level*Backbone Model=BotRGCN, Feature Category=Sentiments2025.11 | 93.5 | |
| Dataset-Level*Backbone Model=BotRGCN, Feature Category=Sentiments2025.11 | 92.9 | |
| Model-Level*Backbone Model=BotRGCN, Feature Category=Sentiments2025.11 | 92.9 | |
| Model-Level*Backbone Model=BotRGCN, Feature Category=Values2025.11 | 90.5 | |
| Shortcut_trBackbone Model=BotRGCN, Feature Category=Values2025.11 | 90.4 | |
| Model-Level*Backbone Model=BotRGCN, Feature Category=Topics2025.11 | 90.3 | |
| Dataset-Level*Backbone Model=BotRGCN, Feature Category=Values2025.11 | 89.1 | |
| Text-Level*Backbone Model=BotRGCN, Feature Category=Values2025.11 | 88.1 | |
| AMR+CIGABackbone Model=BotRGCN, Feature Category=Emotions2025.11 | 87 | |
| Shortcut_trBackbone Model=BotRGCN, Feature Category=Sentiments2025.11 | 86.9 | |
| AMR+CIGABackbone Model=BotRGCN, Feature Category=Topics2025.11 | 85 | |
| AMR+CIGABackbone Model=BotRGCN, Feature Category=Sentiments2025.11 | 83.8 | |
| AMR+CIGABackbone Model=BotRGCN, Feature Category=Values2025.11 | 78.5 |