An Unsupervised Approach to Discover Media Frames
Sha Lai, Yanru Jiang, Lei Guo, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
Abstract
Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.- Anthology ID:
- 2022.politicalnlp-1.4
- Volume:
- Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Haithem Afli, Mehwish Alam, Houda Bouamor, Cristina Blasi Casagran, Colleen Boland, Sahar Ghannay
- Venue:
- PoliticalNLP
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 22–31
- Language:
- URL:
- https://aclanthology.org/2022.politicalnlp-1.4
- DOI:
- Cite (ACL):
- Sha Lai, Yanru Jiang, Lei Guo, Margrit Betke, Prakash Ishwar, and Derry Tanti Wijaya. 2022. An Unsupervised Approach to Discover Media Frames. In Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences, pages 22–31, Marseille, France. European Language Resources Association.
- Cite (Informal):
- An Unsupervised Approach to Discover Media Frames (Lai et al., PoliticalNLP 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.politicalnlp-1.4.pdf
- Code
- slai7880/unsupervised-media-frame-discovery