Detecting Stance in Media on Global warming
This repository contains code and data for the paper:
Luo, Y., Card, D. and Jurafsky, D. (2020). Detecting Stance in Media on Global Warming. In Findings of the Association for Computational Linguistics: EMNLP 2020.
- Create and activate a Python 3.6 environment.
pip install -r requirements.txt.
- Re-install neuralcoref with the
pip uninstall neuralcoref pip install neuralcoref --no-binary neuralcoref
- Download SpaCy's English model:
python -m spacy download en
- Update the
config.jsonfile with your local OS variables.
- Our dataset GWSD itself can be accessed via
GWSD.tsvin the main directory. The dataset contains tab-separated fields for each of the following:
sentence: the sentence
worker_7: ratings from each of the 8 workers for the stance of the sentence
disagree: the probability that the sentence expresses disagreement with the target (that climate change/global warming is a serious concern), as estimated by our Bayesian model
agree: ditto for the "agrees" label
neutral: ditto for the "neutral" label
guid: a unique ID for each sentence
in_held_out_test: whether the sentence was used in our held-out-test set for model and baseline evaluation
Note: The first 5 rows are the 5 screen sentences we use to make sure that annotators correctly understand the task, and thus do not have estimated probability distributions.
- Our lexicons of framing devices are located in
- The sequence of code to replicate our results can be found in the individual READMEs of the numbered sub-directories.