Media Framing: A typology and Survey of Computational Approaches Across Disciplines

Yulia Otmakhova, Shima Khanehzar, Lea Frermann


Abstract
Framing studies how individuals and societies make sense of the world, by communicating or representing complex issues through schema of interpretation. The framing of information in the mass media influences our interpretation of facts and corresponding decisions, so detecting and analysing it is essential to understand biases in the information we consume. Despite that, framing is still mostly examined manually, on a case-by-case basis, while existing large-scale automatic analyses using NLP methods are not mature enough to solve this task. In this survey we show that despite the growing interest to framing in NLP its current approaches do not capture those aspects which allow to frame, rather than simply convey, the message. To this end, we bring together definitions of frames and framing adopted in different disciplines; examine cognitive, linguistic, and communicative aspects a frame contains beyond its topical content. We survey recent work on computational frame detection, and discuss how framing aspects and frame definitions are (or should) be reflected in NLP approaches.
Anthology ID:
2024.acl-long.822
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15407–15428
Language:
URL:
https://aclanthology.org/2024.acl-long.822
DOI:
Bibkey:
Cite (ACL):
Yulia Otmakhova, Shima Khanehzar, and Lea Frermann. 2024. Media Framing: A typology and Survey of Computational Approaches Across Disciplines. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15407–15428, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Media Framing: A typology and Survey of Computational Approaches Across Disciplines (Otmakhova et al., ACL 2024)
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PDF:
https://aclanthology.org/2024.acl-long.822.pdf