AAAI 2024technical1 citations
Quantifying Political Polarization through the Lens of Machine Translation and Vicarious Offense
Abstract
This talk surveys three related research contributions that shed light on the current US political divide: 1. a novel machine-translation-based framework to quantify political polarization; 2. an analysis of disparate media portrayal of US policing in major cable news outlets; and 3. a novel perspective of vicarious offense that examines a timely and important question -- how well do Democratic-leaning users perceive what content would be deemed as offensive by their Republican-leaning counterparts or vice-versa?
BibTeX
@article{KhudaBukhsh_2024, title={Quantifying Political Polarization through the Lens of Machine Translation and Vicarious Offense}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30288}, DOI={10.1609/aaai.v38i20.30288}, abstractNote={This talk surveys three related research contributions that shed light on the current US political divide: 1. a novel machine-translation-based framework to quantify political polarization; 2. an analysis of disparate media portrayal of US policing in major cable news outlets; and 3. a novel perspective of vicarious offense that examines a timely and important question -- how well do Democratic-leaning users perceive what content would be deemed as offensive by their Republican-leaning counterparts or vice-versa?}, number={20}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={KhudaBukhsh, Ashiqur R.}, year={2024}, month={Mar.}, pages={22672-22672} }