Quantify the Political Bias in News Edits: Experiments with Few-Shot Learners (Student Abstract)

Authors

  • Preetika Verma Birla Institute of Technology and Science, Pilani
  • Hansin Ahuja Google India
  • Kokil Jaidka National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v37i13.27037

Keywords:

Bias, News, Edits

Abstract

The rapid growth of information and communication technologies in recent years, and the different forms of digital connectivity, have profoundly affected how news is generated and consumed. Digital traces and computational methods offer new opportunities to model and track the provenance of news. This project is the first study to characterize and predict how prominent news outlets make edits to news frames and their implications for geopolitical relationships and attitudes. We evaluate the feasibility of training few-shot learners on the editing patterns of articles discussing different countries, for understanding their wider implications in preserving or damaging geopolitical relationships.

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Published

2024-07-15

How to Cite

Verma, P., Ahuja, H., & Jaidka, K. (2024). Quantify the Political Bias in News Edits: Experiments with Few-Shot Learners (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16354-16355. https://doi.org/10.1609/aaai.v37i13.27037