INDEPROP: Information-Preserving De-Propagandization of News Articles (Student Abstract)

Aaryan Bhagat, Faraaz Mallick, Neel Karia, Ayush Kaushal

AAAI 2022 pp. 12915-12916

doi:10.1609/AAAI.V36I11.21594 /aaai/2022/bhagat2022aaai-indeprop/

Abstract

We propose INDEPROP, a novel Natural Language Processing (NLP) application for combating online disinformation by mitigating propaganda from news articles. INDEPROP (Information-Preserving De-propagandization) involves fine-grained propaganda detection and its removal while maintaining document level coherence, grammatical correctness and most importantly, preserving the news articles’ information content. We curate the first large-scale dataset of its kind consisting of around 1M tokens. We also propose a set of automatic evaluation metrics for the same and observe its high correlation with human judgment. Furthermore, we show that fine-tuning the existing propaganda detection systems on our dataset considerably improves their generalization to the test set.

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Cite

Text

Bhagat et al. "INDEPROP: Information-Preserving De-Propagandization of News Articles (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21594

Markdown

[Bhagat et al. "INDEPROP: Information-Preserving De-Propagandization of News Articles (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/bhagat2022aaai-indeprop/) doi:10.1609/AAAI.V36I11.21594

BibTeX

@inproceedings{bhagat2022aaai-indeprop,
  title     = {{INDEPROP: Information-Preserving De-Propagandization of News Articles (Student Abstract)}},
  author    = {Bhagat, Aaryan and Mallick, Faraaz and Karia, Neel and Kaushal, Ayush},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12915-12916},
  doi       = {10.1609/AAAI.V36I11.21594},
  url       = {https://mlanthology.org/aaai/2022/bhagat2022aaai-indeprop/}
}