Hayawi

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

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• authors: Kadhim Hayawi, Sujith Mathew, Neethu Venugopal, Mohammad M. Masud, Pin‑Han Ho

• link: https://link.springer.com/article/10.1007/s13278-022-00869-w

• file structure:

├── boto-19/
├── cresci-18/
├── cresci-17/
├── cresci-19/
├── twibot-22/
├── midterm-18/
├── cresci-15/
├── twibot-20/
├── gilani-17/
├── run.py 
└── Hayawi.md # README

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

How to reproduce:

1. preprocess the dataset and train this model by running

   python run.py {dataset name}

   twibot-22 for example :

   python run.py twibot-22

   • dataset names:
     • boto-19
     • cresci-18
     • cresci-17
     • cresci-19
     • twibot-22
     • midterm-18
     • cresci-15
     • twibot-20
     • gilani-17

Result:

dataset		acc	precison	recall	f1
Cresci-2015	mean	0.8427	0.9296	0.7931	0.8556
Cresci-2015	std	0.0002	0.0003	0.0002	0.0001
Twibot-20	mean	0.7314	0.7161	0.8350	0.7705
Twibot-20	std	0.0001	0.0001	0.0004	0.0002
Twibot-22	mean	0.7650	0.8000	0.1499	0.2474
Twibot-22	std	0.0007	0.0027	0.0005	0.0008
Gilani-17	mean	0.5270	0.5144	0.2800	0.3467
Gilani-17	std	0.0002	0.0005	0.0013	0.0011
Cresci-2017	mean	0.9078	0.9547	0.9219	0.9378
Cresci-2017	std	0.0001	0.0001	0.0003	0.0001
Cresci-stock-2018	mean	0.5002	0.5073	0.7116	0.6075
Cresci-stock-2018	std	0.0002	0.0003	0.0007	0.0006
Cresci-rtbust-2019	mean	0.5118	0.4882	0.8125	0.6087
Cresci-rtbust-2019	std	0.0002	0.0001	0.0009	0.0003
Midterm-2018	mean	0.8459	0.8530	0.9864	0.9148
Midterm-2018	std	0.0000	0.0000	0.0000	0.0000
Botometer-feedback-2019	mean	0.7698	0.2500	0.1778	0.2049
Botometer-feedback-2019	std	0.0002	0.0006	0.0006	0.0006

baseline	acc on Twibot-22	f1 on Twibot-22	type	tags
Hayawi et al.	0.7650	0.2474	F	lstm