readme.md

Online Human-Bot Interactions: Detection, Estimation, and Characterization

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• authors: Onur Varol, Emilio Ferrara, Clayton A. Davis, Filippo Menczer, Alessandro Flammini

• link: https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15587/14817

• file structure:

├── cresci-2015
│   └── train.py  # train model on cresci-2015
├── preprocess.py # convert raw dataset into standard format
├── sta.py
├── Twibot-20    
│   └── train.py  # train model on Twibot-20
└── Twibot-22
    └── train.py  # train model on Twibot-22

• implement details: “Sentiment”, “Timing” features are discarded since required information is not included in datasets.

How to reproduce:

1. specify the dataset b y running dataset=Twibot-22 (Twibot-22 for example) ;

2. convert the raw dataset into standard format by running

   python preprocess.py --datasets ${dataset}

   this command will create related features in corresponding directory.

3. train random forest model by running:

   cd ${dataset} && python train.py > result.txt

   the final result will be saved into result.txt

Result:

random seed: 100, 200, 300, 400, 500

dataset		acc	precison	recall	f1
Cresci-2015	mean	0.9316	0.9222	0.9740	0.9473
Cresci-2015	std	0.0054	0.0066	0.0090	0.0042
Twibot-20	mean	0.7874	0.7804	0.8437	0.8108
Twibot-20	std	0.0055	0.0061	0.0067	0.0048
Twibot-22	mean	0.7392	0.7574	0.1683	0.2754
Twibot-22	std	0.0002	0.0031	0.0021	0.0026

baseline	acc on Twibot-22	f1 on Twibot-22	type	tags
Varol et al.	0.7392	0.2754	P T	random forest