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.

@inproceedings{luo-etal-2020-detecting,
    title = "Detecting Stance in Media On Global Warming",
    author = "Luo, Yiwei  and
      Card, Dallas  and
      Jurafsky, Dan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.findings-emnlp.296",
    doi = "10.18653/v1/2020.findings-emnlp.296",
    pages = "3296--3315",
    abstract = "Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, {``}Leading scientists agree that global warming is a serious concern,{''} framing a clause which affirms their own stance ({``}that global warming is serious{''}) as an opinion endorsed (''[scientists] agree{''}) by a reputable source ({``}leading{''}). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: {``}Mistaken scientists claim [...].'' Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other{'}s opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author{'}s own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.",
}

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

4. Download SpaCy's English model: python -m spacy download en
5. 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.