---
title: "Stance Detection in COVID-19 Tweets — Glandt 2021"
description: "The paper addresses stance detection on Twitter for COVID-19 public-health topics."
authors: ["Kyle Glandt", "Sarthak Khanal", "Yingjie Li", "Doina Caragea", "Cornelia Caragea"]
year: 2021
venue: "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/2021.acl-long.127/"
doi: "10.18653/v1/2021.acl-long.127"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/glandt-2021-stance-detection-covid-19-tweets/"
---
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# Stance Detection in COVID-19 Tweets

**Authors:** Kyle Glandt, Sarthak Khanal, [Yingjie Li](https://md.research.elamur.ai/free/authors/yingjie-li/), Doina Caragea, [Cornelia Caragea](https://md.research.elamur.ai/free/authors/cornelia-caragea/) · **Year:** 2021 · **Venue:** Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.acl-long.127/) · [PDF](https://aclanthology.org/2021.acl-long.127.pdf) · [DOI 10.18653/v1/2021.acl-long.127](https://doi.org/10.18653/v1/2021.acl-long.127) · [OpenAlex](https://openalex.org/W3173991475)

**Citations:** 102 citations (openalex-snapshot · 2026-10-14) · [71](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses stance detection on Twitter for COVID-19 public-health topics. Knowing whether people support or oppose measures such as mask-wearing or lockdowns can help officials judge how well mandates are followed and spot "pandemic fatigue" early. Benchmarks for this domain were lacking, so the authors built COVID-19-Stance, a manually labelled collection of 6,133 English tweets covering four targets. Each tweet is labelled as in-favor, against or neither. The tweets also carry extra annotations for whether the opinion is explicit or implicit and for overall sentiment.

On this data they compared established supervised stance models, including Bi-LSTM, Kim-CNN, GCAE, TAN, ATGRU and BERT. They also tested two ways to compensate for scarce labels: noisy-student self-training on unlabelled tweets (BERT-NS) and domain adaptation from SemEval2016 Task 6 (BERT-DAN). The pre-trained COVID-Twitter-BERT clearly outperformed the other supervised baselines. The NS and DAN extensions added further gains mainly for targets with more labelled examples. The resource gives a starting benchmark for health-related stance research.

## Key points
- **Dataset and targets:** The 6,133 tweets in COVID-19-Stance are spread across four targets, namely "Keeping Schools Closed", "Wearing a Face Mask", "Stay at Home Orders" and "Anthony S. Fauci, M.D.".
- **Release:** Only tweet IDs and labels are shared, in line with Twitter's privacy policy, and code is available on GitHub.
- **Supervised baselines:** BERT was significantly stronger than all other models. Surprisingly, the target-aware attention models TAN and ATGRU did worse overall than a plain Bi-LSTM.
- **Self-training vs. domain adaptation:** BERT-DAN, which adapts from SemEval2016 Task 6, scored best on the Fauci target and matched BERT-NS on face masks. Plain BERT did better on "Stay at Home Orders", one of the smaller targets. This suggests domain adaptation needs more in-domain labels and self-training needs a good teacher.
- **Error analysis:** The models handle explicit stances well but struggle with sarcasm and humour. They also over-rely on stock phrases such as "form of government control". BERT-NS tends to predict FAVOR when a tweet's sentiment is positive.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

71 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 58 not yet.

- [Can Large Language Models Address Open-Target Stance Detection?](https://md.research.elamur.ai/free/papers/akash-2025-can-large-language-models-address/)
- [Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection](https://md.research.elamur.ai/free/papers/arakelyan-2023-topic-guided-sampling-data-efficient/)
- [Cross-Domain Label-Adaptive Stance Detection](https://md.research.elamur.ai/free/papers/hardalov-2021-cross-domain-label-adaptive-stance/)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://md.research.elamur.ai/free/papers/he-2022-infusing-knowledge-wikipedia-enhance-stance/)
- [Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation](https://md.research.elamur.ai/free/papers/li-2021-improving-stance-detection-multi-dataset/)
- [Distilling Calibrated Knowledge for Stance Detection](https://md.research.elamur.ai/free/papers/li-2023-distilling-calibrated-knowledge-stance-detection/)
- [A New Direction in Stance Detection: Target-Stance Extraction in the Wild](https://md.research.elamur.ai/free/papers/li-2023-new-direction-stance-detection-target/)
- [Stance Detection on Social Media with Background Knowledge](https://md.research.elamur.ai/free/papers/li-2023-stance-detection-social-media-background/)
- [Guiding Computational Stance Detection with Expanded Stance Triangle Framework](https://md.research.elamur.ai/free/papers/liu-2023-guiding-computational-stance-detection-expanded/)
- [Multilingual Target-Stance Extraction](https://md.research.elamur.ai/free/papers/mines-2026-multilingual-target-stance-extraction/)
- [A Challenge Dataset and Effective Models for Conversational Stance Detection](https://md.research.elamur.ai/free/papers/niu-2024-challenge-dataset-effective-models-conversational/)
- [Adversarial Network with External Knowledge for Zero-Shot Stance Detection](https://md.research.elamur.ai/free/papers/wang-2023-adversarial-network-external-knowledge-zero/)
- [C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection](https://md.research.elamur.ai/free/papers/zhao-2023-c-stance-large-dataset-chinese/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{glandt2021stance,
  title = {{Stance Detection in COVID-19 Tweets}},
  author = {Glandt, Kyle and Khanal, Sarthak and Li, Yingjie and Caragea, Doina and Caragea, Cornelia},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.127},
  url = {https://doi.org/10.18653/v1/2021.acl-long.127}
}
```

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