Hedge detection as a lens on framing in the GMO debates: A position paper
Eunsol Choi, Chenhao Tan, Lillian Lee, Cristian Danescu-Niculescu-Mizil, and Jennifer Spindel
Proceedings of the ACL Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, pp. 70--79, 2012

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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.

Paper: pdf, arxiv version

Data: GMOHedging_v1.0.zip (5.72MB): includes the paper and this README (v1.0)

Presentation: ppt, pdf


   author = {Eunsol Choi and Chenhao Tan and Lillian Lee and Cristian Danescu-Niculescu-Mizil and Jennifer Spindel},
   title = {Hedge detection as a lens on framing in the {GMO} debates: A position paper},
   booktitle = {Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics},
   pages = {70--79},
   year = 	 {2012} }

This material is based upon work supported in part by US NSF grants IIS-0910664 and IIS-1016099, a US NSF graduate fellowship to Jennifer Spindel, Google, and Yahoo! Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other sponsors.

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