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Knauth

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  • Language-Agnostic Twitter Bot Detection
├── generate_features.py # convert raw dataset into standard format
└── Adaboost.py # train model on every dataset
  • implement details:
    • We ignored the content-based features due to the lack of the information in our datasets
    • We did not use the SMOTEENN method to balance the negative samples, since our datasets are well balanced and adding SMOTEENN did not improve the results
    • All datasets except Twibot-20 lack some of the core features in account based features such as default profile and geo-enabled, and some of the users’ data is missing ( i.e. no friends count t, follower count etc. ) and were set to 0 in our experiments

How to reproduce:

the data has been preprocessed and stored in folders e.g. /Twibot-20 change the dataset name dataset='Twibot-22' and run Adaboost.py

you can check the results in /results/dataset.log

Result:

dataset acc precison recall f1
Twibot-22 mean 0.7125 0.7990 0.5214 0.3709
Twibot-20 mean 0.8191 0.9656 0.7630 0.8524
botometer-feedback-2019 mean 0.7597 0.3171 0.5909 0.4127
cresci-rtbust-2019 mean 0.5000 0.5741 0.5124 0.5415
cresci-stock-2018 mean 0.8874 0.9989 0.8883 0.9403
midterm-2018 mean 0.8393 0.9991 0.8399 0.9126
cresci-2017 mean 0.9022 0.9156 0.9535 0.9342
gilani-2017 mean 0.4967 0.3517 0.4400 0.3910
cresci-2015 mean 0.8592 0.8570 0.9740 0.9118