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

We propose INDEPROP, a novel Natural Language Processing (NLP) application for combating online disinformation by mitigating propaganda from news articles. IN- DEPROP (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.

Authors

• Aaryan Bhagat
• Faraaz Mallick
• Neel Karia
• Ayush Kaushal

Keywords

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