Eric P. S. Baumer, Elisha Elovic, Ying Qin, Francesca Polletta, & Geri K. Gay. (2015). Testing and Comparing Computational Approaches for Identifying the Language of Framing in Political News. in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL – HLT) (pp. 1472–1482). Denver, CO.
The subconscious influence of framing on perceptions of political issues is well-document in political science and communication research. A related line of work suggests that drawing attention to framing may help reduce such framing effects by enabling frame reflection, critical examination of the framing underlying an issue. However, definite guidance on how to identify framing does not exist. This paper presents a technique for identifying frame-invoking language. The paper first describes a human subjects pilot study that explores how individuals identify framing and informs the design of our technique. The paper then describes our data collection and annotation approach. Results show that the best performing classifiers achieve performance comparable to that of human annotators, and they indicate which aspects of language most pertain to framing. Both technical and theoretical implications are discussed.
Data Set – This includes all 75 annotated articles, as well as descriptions of the format and instructions on use.