Quantifying Political Polarization Through the Lens of Machine Translation and Vicarious Offense

AAAI 2024 pp. 22672

doi:10.1609/AAAI.V38I20.30288 /aaai/2024/khudabukhsh2024aaai-quantifying/

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?

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Cite

Text

KhudaBukhsh. "Quantifying Political Polarization Through the Lens of Machine Translation and Vicarious Offense." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I20.30288

Markdown

[KhudaBukhsh. "Quantifying Political Polarization Through the Lens of Machine Translation and Vicarious Offense." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/khudabukhsh2024aaai-quantifying/) doi:10.1609/AAAI.V38I20.30288

BibTeX

@inproceedings{khudabukhsh2024aaai-quantifying,
  title     = {{Quantifying Political Polarization Through the Lens of Machine Translation and Vicarious Offense}},
  author    = {KhudaBukhsh, Ashiqur R.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {22672},
  doi       = {10.1609/AAAI.V38I20.30288},
  url       = {https://mlanthology.org/aaai/2024/khudabukhsh2024aaai-quantifying/}
}