Official code and data of the paper SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter.
- We propose SeGA to address the challenging but emerging anomalous user detection task on Twitter.
- We introduce preference-aware self-contrastive learning to learn user behaviors via the corresponding posts.
- Extensive experiments on the proposed TwBNT benchmark demonstrate that SeGA significantly outperforms the state-of-the-art methods (+3.5% ∼ 27.6%).
We provide the user IDs and list IDs sampled from Twibot-22 and user labels in this repo.
Download the complete data: https://drive.google.com/drive/folders/1KSR1-5aHx33bDrnRT2QxLT20n2-vCVsH?usp=drive_link
- Encode node features
python preprocess-sega.py
- Run SeGA with list nodes and pre-train strategy
python main.py --lst --pretrain
If you use our dataset or find our project is relevant to your research, please consider citing our work!
@article{SeGA_AAAI2024,
author = {Ying{-}Ying Chang and
Wei{-}Yao Wang and
Wen{-}Chih Peng},
title = {SeGA: Preference-Aware Self-Contrastive Learning with Prompts for
Anomalous User Detection on Twitter},
journal = {CoRR},
volume = {abs/2312.11553},
year = {2023}
}