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You Are Known by Your Friends: Leveraging Network Metrics for Bot Detection in Twitter

├── feature_engineering.py      # generate required features
├── feature_twibot22.py         # generate required features for Twibot-22 dataset
└── rand_forest.py              # train a random forest model on given dataset
  • implement details: In all datasets except for Twibot-22, only following relationship is available so only one relationship i.e. following relationship is leveraged to construct ego networks.

How to reproduce:

  1. specify the dataset b y running dataset=Twibot-20 (Twibot-20 for example) ;

  2. train random forest model by running:

    python rand_forest.py --dataset ${dataset}

Result:

random seed: 0, 100, 200, 300, 400

dataset acc precison recall f1
Cresci-2015 mean 0.9686 0.9529 1.0000 0.9758
Cresci-2015 std 0.0112 0.0162 0.0000 0.0084
Cresci-2017 mean 0.7804 0.7755 1.0000 0.8735
Cresci-2017 std 0.0103 0.0081 0.0000 0.0052
Twibot-20 mean 0.7589 0.7264 0.8894 0.7997
Twibot-20 std 0.0047 0.0052 0.0059 0.0034
baseline acc on Twibot-22 f1 on Twibot-22 type tags
FriendBot / / F T G random forest