Repository for the EMNLP 2020 Findings paper:
STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval

Link to the paper: STANDER: An Expert-Annotated Datasetfor News Stance Detection and Evidence Retrieval

Please use the following citation:

@inproceedings{conforti2020stander,
          title={STANDER: An Expert-Annotated Datasetfor News Stance Detection and Evidence Retrieval},
          author={Conforti, Costanza and Berndt, Jakob and Pilehvar, Mohammad Taher and Giannitsarou, Chryssi and Toxvaerd, Flavio and Collier, Nigel} 
          booktitle={Findings of EMNLP},
          year={2020}
        }

Dataset

STANDER is a large dataset of news articles in English from high-reputation sources which discuss four recent mergers and acquisitions (M&A) operations between major healthcare companies in the US. The news articles are annotated by experts and labeled for stance detection and fine-grained evidence retrieval.

STANDER contains the same targets as in the Twitter stance detection WT–WT corpus WT–WT corpus (Conforti et al., 2020). The union of both corpora thus provides a great opportunity for studying the interplay between authoritative and user-generated signals.

Considered M&A operations

Operation	Buyer	Target	Industry
CVS_AET	CVS Health	Aetna	Healthcare
CI_ESRX	Cigna	Express Scripts	Healthcare
ANTM_CI	Anthem	Cigna	Healthcare
AET_HUM	Aetna	Humana	Healthcare

Contacts

• {cc918, jb2088} @cam.ac.uk
• Cambridge Language Technology Lab
• Cambridge Faculty of Economics