Efthimion

Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots

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• authors: Efthimion et al.

• link: https://scholar.smu.edu/datasciencereview/vol1/iss2/5/

• file structure:

└── feature.py

• implement details: We abdicate the Levenshtein distance for time consumption problem.

How to reproduce:

1. run

   python feature.py

   different datasets are optional in the code.

Result:

dataset		acc	precison	recall	f1
Botometer-feedback-2019	mean	0.6981	0.0000	0.0000	0.0000
Botometer-feedback-2019	std	0.0000	0.0000	0.0000	0.0000
Cresci-2015	mean	0.9252	0.9382	0.9438	0.9410
Cresci-2015	std	0.0000	0.0000	0.0000	0.0000
Cresci-2017	mean	0.8796	0.9458	0.8923	0.9183
Cresci-2017	std	0.0000	0.0000	0.0000	0.0000
Cresci-rtbust-2019	mean	0.6765	0.6829	0.7568	0.7179
Cresci-rtbust-2019	std	0.0000	0.0000	0.0000	0.0000
Cresci-stock-2018	mean	0.7076	0.8275	0.5802	0.6821
Cresci-stock-2018	std	0.0000	0.0000	0.0000	0.0000
gilani-2017	mean	0.5551	0.3750	0.0280	0.0522
gilani-2017	std	0.0000	0.0000	0.0000	0.0000
midterm-2018	mean	0.9339	0.9801	0.9404	0.9598
midterm-2018	std	0.0000	0.0000	0.0000	0.0000
Twibot-20	mean	0.6281	0.6420	0.7063	0.6726
Twibot-20	std	0.0000	0.0000	0.0000	0.0000
Twibot-22	mean	0.7408	0.7778	0.1676	0.2758
Twibot-22	std	0.0000	0.0000	0.0000	0.0000

baseline	acc on Twibot-22	f1 on Twibot-22	type	tags
efthimion	0.7408	0.2758	F T	efthimion