readme.md

Twitter bot detection using bidirectional long short-term memory neural networks and word embeddings

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• authors: Feng Wei, Uyen Trang Nguyen

• link: https://arxiv.org/pdf/2002.01336.pdf

• file structure:

├── Twibot-20
│   ├── preprocess.py 
│   ├── bilstm_attention.py 
│   └── data_processor.py   
└── Twibot-22
    ├── preprocess.py 
    ├── bilstm_attention.py 
    └── data_processor.py 

• implement details: We set some parameters by ourselves. We set the embedding size to 200, the max length of sentence to 64, the hidden layer to 64, and the size of vocabulary to 5000.

How to reproduce:

1. convert the raw dataset into standard format by running

   python preprocess.py

   this command will create related features in corresponding directory.

2. train bilstm model by running:

   python bilstm_attention.py

   the final result will be showed.

Result:

random seed: 0, 100, 200, 300, 400

dataset		acc	precison	recall	f1
Cresci-2015	mean	0.961	0.917	0.753	0.827
Cresci-2015	std	0.014	0.017	0.015	0.022
Cresci-2017	mean	0.893	0.859	0.721	0.784
Cresci-2017	std	0.007	0.019	0.015	0.017
Twibot-20	mean	0.713	0.610	0.540	0.573
Twibot-20	std	0.016	0.021	0.027	0.031
Twibot-22	mean	0.702	0.627	0.468	0.536
Twibot-22	std	0.012	0.018	0.014	0.013

baseline	acc on Twibot-22	f1 on Twibot-22	type	tags
Varol et al.	0.702	0.536	T