RoBERTa

RoBERTa

---

• authors: Liu et al.

• link: https://arxiv.org/pdf/1907.11692.pdf%5C

• file structure:

├── cresci-2015
│   ├── id_list.py
│   ├── label_list.py
│   ├── train.py
│   ├── des_embedding.py
│   ├── tweets_tensor.py
│   └── user_tweets_dict.py
├── cresci-2017
│   ├── id_list.py
│   ├── label_list.py
│   ├── train.py
│   ├── des_embedding.py
│   ├── tweets_tensor.py
│   └── user_tweets_dict.py
├── Twibot-20
│   ├── id_list.py
│   ├── label_list.py
│   ├── train.py
│   ├── des_
│   ├── tweets_tensor.py
│   └── user_tweets_dict.py
└── Twibot-22
    ├── id_list.py
    ├── label_list.py
    ├── des_embedding.py
    ├── train.py
    ├── tweets_tensor.py
    └── user_tweets_dict.py

• implement details: For Twibot-22, users' tweet counts could be cut to 20 for time consumption issue.

How to reproduce:

1. run id_list.py first to generate id_list.json and run tweets_tensor.py,des_embedding.py to generate tweets' and des' embeddings. run label_list.py to get label.

2. finetune RoBERTa by running train.py(need to change the path of data in the code)

Result:

dataset		acc	precison	recall	f1
Cresci-2015	mean	0.9701	0.9758	0.9411	0.9586
Cresci-2015	std	0.0013	0.0027	0.0058	0.0019
Cresci-2017	mean	0.9719	0.9243	0.9627	0.9430
Cresci-2017	std	0.0009	0.0099	0.0105	0.0018
Twibot-20	mean	0.7555	0.7388	0.7238	0.7309
Twibot-20	std	0.0018	0.0106	0.0205	0.0059
Twibot-22	mean	0.7207	0.6328	0.1227	0.2053
Twibot-22	std	0.0016	0.0090	0.0122	0.0171

baseline	acc on Twibot-22	f1 on Twibot-22	type	tags
RoBERTa	0.7207	0.2053	F T	RoBERTa