khuangaf/fakingfakenews

Official implementation of the ACL 2023 paper: "Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation"

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation (ACL 2023)

Kung-Hsiang (Steeve) Huang, Kathleen McKeown, Preslav Nakov, Yejin Choi and Heng Ji

Abstract

Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What limits the successful transfer between them is the sizable gap between machine-generated fake news and human-authored ones, including the notable differences in terms of style and underlying intent. With this in mind, we propose a novel framework for generating training examples that are informed by the known styles and strategies of human-authored propaganda.
Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles, while also incorporating propaganda techniques, such as appeal to authority and loaded language. In particular, we create a new training dataset, PropaNews, with 2,256 examples, which we release for future use. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62--7.69% F1 score on two public datasets.

How to use this repo

Please refer to the README within each model directory for specific instructions on how to run them.

Data

The generated data and the test data used in our experiments are included in the data folder. train.jsonl, dev.jsonl, and test.jsonl are our generated data. snopes_test.jsonl and politifact_test.jsonl contain real and fake news from Snopes and PolitiFact.

Citation

If you find this work useful, please consider citing:

@inproceedings{huang2023faking,
  title     = "Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation",
  author    = "Huang, Kung-Hsiang, Kathleen McKeown, Preslav Nakov, Yejin Choi, and Heng Ji",
  year = "2023",
  month= july,
  booktitle = "Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics",
  publisher = "Association for Computational Linguistics",
}

fakingfakenews's Issues

Disinformation Generation

Hello, I learned a lot from your published paper, and I am very interested in the part of Disinformation Generation. Could you please share the code to realize this with me? Thank you very much and look forward to your reply.