Santos

Uncovering Social Media Bots:a Transparency-focused Approach

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• authors: Eric F. Santos, Danilo S. Carvalho, Livia Ruback, Jonice Oliveira

• link: https://dl.acm.org/doi/pdf/10.1145/3308560.3317599

• file structure:

├── cresci-2015
├── dlc2015.py # convert raw dataset into standard format and save them
├── Twibot-20    
├── dltwi20.py # convert raw dataset into standard format and save them
├── cresci-2017
├── dlc2017.py # convert raw dataset into standard format and save them
├── midterm-2018
├── dlm2018.py # convert raw dataset into standard format and save them
├── gilani-2017    
├── dlg2017.py # convert raw dataset into standard format and save them
├── cresci-stock-2018
├── dlcs2018.py # convert raw dataset into standard format and save them
├── cresci-rtbust-2019
├── dlcr2019.py # convert raw dataset into standard format and save them
├── botometer-feedback-2019   
├── dlbf2019.py # convert raw dataset into standard format and save them
└── dt.py       # train a decision tree

• implement details: Since some datasets don’t contain contents of tweets which users posted, we extracted features from user’s description if the dataset we use doesn’t have content of tweets.

How to reproduce:(Twibot-20 For example)

1. convert the raw dataset into standard format by running

   python dltwi20.py

   this command will create related features in corresponding directory.

2. open:

   python gcntwi22.py

   then change filename into features created by first command and change the path to datasets in codes in line31-line34

Result:

dataset		acc	precison	recall	f1
Twibot-20	mean	0.5866	0.6273	0.5813	0.6034
Twibot-20	std	0.0000	0.0000	0.0000	0.0000
Cresci-2015	mean	0.7084	0.7286	0.8580	0.7880
Cresci-2015	std	0.0000	0.0000	0.0000	0.0000
Cresci-2017	mean	0.7384	0.8171	0.8440	0.8303
Cresci-2017	std	0.0000	0.0000	0.0000	0.0000
midterm-2018	mean	0.8661	0.8805	0.9724	0.9242
midterm-2018	std	0.0001	0.0000	0.0000	0.0000
gilani-2017	mean	0.5144	0.3226	0.0935	0.1449
gilani-2017	std	0.0000	0.0000	0.0004	0.0000
cresci-stock-2018	mean	0.6245	0.6539	0.6495	0.6517
cresci-stock-2018	std	0.0000	0.0000	0.0000	0.0000
cresci-rtbust-2019	mean	0.7353	0.7568	0.7568	0.7568
cresci-rtbust-2019	std	0.0000	0.0000	0.0000	0.0000
botometer-feedback-2019	mean	0.7170	0.5000	0.1333	0.2105
botometer-feedback-2019	std	0.0000	0.0000	0.0000	0.0000

baseline	acc on Twibot-22	f1 on Twibot-22	type	tags
Santos et al.	/	/	F T	decision tree