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MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters

Dataset

The troll data are download through Twitter (https://transparency.twitter.com/en/reports/moderation-research.html).

Running Environment

This repo is built upon a local copy of transformers==3.0.0. This repo has been tested on torch==1.4.0 with python 3.7 and CUDA 10.1.

To start, create a new environment and install:

conda create -n metaTroll python=3.7
conda activate metaTroll
cd metaTroll
pip install -e .

Baselines:

`MODEL`	GITHUB
Induct	modified based on (https://github.com/zhongyuchen/few-shot-text-classification)
HATT	source code from authors (https://github.com/thunlp/HATT-Proto)
DS	source code from authors (https://github.com/YujiaBao/Distributional-Signatures)

Further Pre-train & Evaluation

To further pre-train base BERT/XLM-R models:

Clone the transformer to local directory

git clone https://github.com/huggingface/transformers.git

For further pre-training the language models:

python transformers/examples/language-modeling/run_language_modeling.py ,
        --output_dir='BERT_DAPT',
        --model_type=bert ,
        --model_name_or_path=bert-base-cased-freeze-we,
        --do_train,
        --overwrite_output_dir,
        --train_data_file='train.txt',
        --do_eval,
        --block_size=512,
        --eval_data_file='vali.txt',
        --mlm"

Publicaton

This is the source code for [MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters]

If you find this code useful, please let us know and cite our paper.
If you have any question, please contact Lin at: s3795533 at student dot rmit dot edu dot au.