Hedge detection as a lens on framing in the GMO debates: A position paper
This is an incremental position paper proposing a new computational approach for analyzing framing in public debates about GMOs, aimed at researchers in computational linguistics and science communication.
The paper tackles the problem of understanding framing in GMO debates by proposing hedge detection as a method to analyze whether pro- and anti-GMO articles differ in adopting a 'scientific' tone, with preliminary findings suggesting hedges occur less frequently in scientific discourse than in popular text, contradicting prior literature.
Understanding the ways in which participants in public discussions frame their arguments is important in understanding how public opinion is formed. In this paper, we adopt the position that it is time for more computationally-oriented research on problems involving framing. In the interests of furthering that goal, we propose the following specific, interesting and, we believe, relatively accessible question: In the controversy regarding the use of genetically-modified organisms (GMOs) in agriculture, do pro- and anti-GMO articles differ in whether they choose to adopt a "scientific" tone? Prior work on the rhetoric and sociology of science suggests that hedging may distinguish popular-science text from text written by professional scientists for their colleagues. We propose a detailed approach to studying whether hedge detection can be used to understanding scientific framing in the GMO debates, and provide corpora to facilitate this study. Some of our preliminary analyses suggest that hedges occur less frequently in scientific discourse than in popular text, a finding that contradicts prior assertions in the literature. We hope that our initial work and data will encourage others to pursue this promising line of inquiry.