Developing a synthetic news corpus to validate generic frame detection methods

Abstract

Frames are a central concept in communication research. Based on our literature review, we propose that frame identification is an act of identifying selected reality and communicative intention. We then highlight the conceptual and methodological issues of frame identification using computational methods. To avoid the correlation between topics and frames, we provide a synthetic dataset for evaluating frames found in multi-topical news content, using the detection of generic frames as a test case. With this dataset, for the first time, we benchmark manual coding and various automatic and semi-supervised methods. Based on the preliminary benchmark results, this study provides evidence that generic frame identification using both manual coding and automatic methods might not be accurate.