A Survey of Computational Framing Analysis Approaches

Mohammad Ali, Naeemul Hassan


Abstract
Framing analysis is predominantly qualitative and quantitative, examining a small dataset with manual coding. Easy access to digital data in the last two decades prompts scholars in both computation and social sciences to utilize various computational methods to explore frames in large-scale datasets. The growing scholarship, however, lacks a comprehensive understanding and resources of computational framing analysis methods. Aiming to address the gap, this article surveys existing computational framing analysis approaches and puts them together. The research is expected to help scholars and journalists gain a deeper understanding of how frames are being explored computationally, better equip them to analyze frames in large-scale datasets, and, finally, work on advancing methodological approaches.
Anthology ID:
2022.emnlp-main.633
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9335–9348
Language:
URL:
https://aclanthology.org/2022.emnlp-main.633
DOI:
10.18653/v1/2022.emnlp-main.633
Bibkey:
Cite (ACL):
Mohammad Ali and Naeemul Hassan. 2022. A Survey of Computational Framing Analysis Approaches. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9335–9348, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Survey of Computational Framing Analysis Approaches (Ali & Hassan, EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.633.pdf