Detecting frames in news headlines and its application to analyzing news framing trends surrounding U.S. gun violence

Abstract

Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as “frames”, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news headlines related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. Our proposed approach sets a new state-of-the-art performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.

The number of times each frame is represented in new article headlines related to gun violence across the 3-year (per month) period. Some of the peaks represent the deadliest mass shootings in the U.S. since 1949 (CNN Library, 2019).

Cite the paper:

Liu, S., Guo, L., Mays, K., Betke, M. & Wijaya, D. (2019). Detecting frames in news headlines and its application to analyzing news framing trends surrounding U.S. gun violence. Paper presented at the annual conference on Computational Natural Language Learning (CoNLL), Hong Kong, November 2019.