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BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks

├── twibot_22/
│   ├── raw_data/
│   ├── processed_data/
│   ├── utils.py
│   ├── model.py
│   ├── preprocess_1.py
│   ├── train.py # train BotRGCN model
│   ├── Dataset.py
│   ├── preprocess.py # preprocess the dataset
│   ├── dataset_tool.py
│   └── preprocess_2.py
├── cresci_15/
│   ├── raw_data/
│   ├── processed_data/
│   ├── utils.py
│   ├── model.py
│   ├── preprocess_1.py
│   ├── train.py # train BotRGCN model
│   ├── Dataset.py
│   ├── preprocess.py # preprocess the dataset
│   ├── dataset_tool.py
│   └── preprocess_2.py
├── twibot_20/
│   ├── raw_data/
│   ├── processed_data/
│   ├── utils.py
│   ├── model.py
│   ├── preprocess_1.py
│   ├── train.py # train BotRGCN model
│   ├── Dataset.py
│   ├── preprocess.py # preprocess the dataset
│   ├── dataset_tool.py
│   └── preprocess_2.py
└── readme.md

  • implement details:

    There are some changes in user numerical properties & user categorical properties due to the lack of relevant data

    1. numerical properties:

      • original: (dim=6)

        followers + followings + favorites + statuses + active_days + screen_name_length

      • twibot-20/cresci-2015/twibot-22: (dim=5)

        followers + followings + statuses + active_days + screen_name_length

    2. categorical properties:

      • original: (dim=11)

        protected + verified + default_profile_image + geo_enabled + contributors_enabled + is_translator + is_translation_enabled + profile_background_image + profile_user_background_image + has_extended_profile + default_profile

      • twibot-20/twibot-22: (dim=3)

        protected + verified + default_profile_image

      • cresci-2015: (dim=1)

        default_profile_image

How to reproduce:

  1. specify the dataset by entering corresponding fold

    • cresci-15 : cd cresci_15/
    • twibot-20 : cd twibot_20/
    • twibot-22 : cd twibot_22/

  2. preprocess the dataset by running

    python preprocess.py

  3. train BotRGCN model by running:

    python train.py

Result:

dataset acc precision recall f1
Cresci-2015 mean 0.9652 0.9551 0.9917 0.9730
Cresci-2015 std 0.0071 0.0102 0.0025 0.0053
Twibot-20 mean 0.8575 0.8452 0.9019 0.8725
Twibot-20 std 0.0068 0.0054 0.0172 0.0073
Twibot-22 mean 0.7966 0.7481 0.4680 0.5750
Twibot-22 std 0.0014 0.0222 0.0276 0.0142

baseline acc on Twibot-22 f1 on Twibot-22 type tags
BotRGCN 0.7966 0.5750 F T G BotRGCN