Measuring Alignment to Authoritarian State Media as Framing Bias

Timothy Niven, Hung-Yu Kao


Abstract
We introduce what is to the best of our knowledge a new task in natural language processing: measuring alignment to authoritarian state media. We operationalize alignment in terms of sociological definitions of media bias. We take as a case study the alignment of four Taiwanese media outlets to the Chinese Communist Party state media. We present the results of an initial investigation using the frequency of words in psychologically meaningful categories. Our findings suggest that the chosen word categories correlate with framing choices. We develop a calculation method that yields reasonable results for measuring alignment, agreeing well with the known labels. We confirm that our method does capture event selection bias, but whether it captures framing bias requires further investigation.
Anthology ID:
2020.nlp4if-1.2
Volume:
Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
NLP4IF
SIG:
Publisher:
International Committee on Computational Linguistics (ICCL)
Note:
Pages:
11–21
Language:
URL:
https://aclanthology.org/2020.nlp4if-1.2
DOI:
Bibkey:
Cite (ACL):
Timothy Niven and Hung-Yu Kao. 2020. Measuring Alignment to Authoritarian State Media as Framing Bias. In Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 11–21, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).
Cite (Informal):
Measuring Alignment to Authoritarian State Media as Framing Bias (Niven & Kao, NLP4IF 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.nlp4if-1.2.pdf
Code
 doublethinklab/nlp4if2020p