SeGA (AAAI 2024)

Official code and data of the paper SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter.

Overview

• 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%).

Data

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

Reproducing SeGA

To reproduce the SeGA model, follow these steps:

• Encode node features

python preprocess-sega.py

• Run SeGA with list nodes and pre-train strategy

python main.py --lst --pretrain

Reference

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}
}