INDEPROP: Information-Preserving De-propagandization of News Articles (Student Abstract)
Keywords:Applications Of AI, Natural Language Processing, SNLP: Applications, AI For Social Good, Propaganda, Detection, Mitigation
AbstractWe 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.
How to Cite
Bhagat, A., Mallick, F., Karia, N., & Kaushal, A. (2022). INDEPROP: Information-Preserving De-propagandization of News Articles (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12915-12916. https://doi.org/10.1609/aaai.v36i11.21594
AAAI Student Abstract and Poster Program