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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.

BibTex TBA

Getting started

  1. Create and activate a Python 3.6 environment.
  2. Run pip install -r requirements.txt.
  3. Re-install neuralcoref with the --no-binary option:
pip uninstall neuralcoref
pip install neuralcoref --no-binary neuralcoref
  1. Download SpaCy's English model: python -m spacy download en
  2. Update the config.json file with your local OS variables.

Repository structure

  • Our dataset GWSD itself can be accessed via GWSD.tsv in the main directory. The dataset contains tab-separated fields for each of the following:
    1. sentence: the sentence
    2. worker_0, ..., worker_7: ratings from each of the 8 workers for the stance of the sentence
    3. 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
    4. agree: ditto for the "agrees" label
    5. neutral: ditto for the "neutral" label
    6. guid: a unique ID for each sentence
    7. 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 4_analyses/lexicons.
  • The sequence of code to replicate our results can be found in the individual READMEs of the numbered sub-directories.

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Code and data for "Detecting Stance in Media on Global Warming".

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