---
title: "A Challenge Dataset and Effective Models for — Niu 2024"
description: "Most stance detection research labels single, isolated posts."
authors: ["Fuqiang Niu", "Min Yang", "Ang Li", "Baoquan Zhang", "Xiaojiang Peng", "Bowen Zhang"]
year: 2024
venue: "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)"
source: "https://aclanthology.org/2024.lrec-main.11/"
arxiv: "2403.11145"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/niu-2024-challenge-dataset-effective-models-conversational/"
---
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# A Challenge Dataset and Effective Models for Conversational Stance Detection

**Authors:** Fuqiang Niu, [Min Yang](https://md.research.elamur.ai/free/authors/min-yang/), [Ang Li](https://md.research.elamur.ai/free/authors/ang-li/), Baoquan Zhang, Xiaojiang Peng, [Bowen Zhang](https://md.research.elamur.ai/free/authors/bowen-zhang/) · **Year:** 2024 · **Venue:** Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.lrec-main.11/) · [PDF](https://aclanthology.org/2024.lrec-main.11.pdf) · [arXiv 2403.11145](https://arxiv.org/abs/2403.11145) · [OpenAlex](https://openalex.org/W7131796596)

**Citations:** 3 citations (openalex-snapshot · 2026-10-14) · 9 in our library, of the 140673 papers whose bibliographies we read

## Summary
Most stance detection research labels single, isolated posts. On social media, however, opinions often appear in long reply threads, where a comment's stance can depend on earlier messages. The two existing conversational stance detection (CSD) benchmarks have clear limits. SRQ covers only direct replies, and CANT-CSD is a small Cantonese corpus made up mostly of shallow threads.

The authors build MT-CSD, an English multi-turn, multi-target dataset with human annotation. Comments are labeled Favor, Against or None toward a given target. They also propose GLAN (global-local attention network), which combines three components:
- a global attention branch for long-range context
- a CNN branch for short, local cues
- a GCN branch over the reply structure that models sub-discussions

They compare GLAN with fine-tuned neural models, prompt-tuned PLMs and in-context learning with LLMs. Even the best system, GLAN, reaches only about 50% accuracy, so the task remains far from solved. The dataset also supports cross-target stance transfer. This makes it a realistic testbed for studying stance in threaded discussions.

## Key points
- MT-CSD has 15,876 annotated instances, and the authors describe it as the largest human-labeled English CSD dataset so far.
- About 76% of instances (75.99%) are deeper than four turns. Compared with CANT-CSD, MT-CSD has more than 12 times as many depth-4 instances; in CANT-CSD, only 6.3% of data exceeds three turns.
- Two main difficulties are implicit references to the target inside sub-threads and the need to resolve coreference using context.
- GLAN's best accuracy is 50.47%. Baselines cover three families: fine-tuning, prompt-tuning with PLMs, and in-context learning with LLMs.
- The code, data and models are released at github.com/nfq729/MT-CSD.

*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/)

## Cite

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

```bibtex
@inproceedings{niu2024challenge,
  title = {{A Challenge Dataset and Effective Models for Conversational Stance Detection}},
  author = {Niu, Fuqiang and Yang, Min and Li, Ang and Zhang, Baoquan and Peng, Xiaojiang and Zhang, Bowen},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/55ng7k3tghj3},
  eprint = {2403.11145},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.63317/55ng7k3tghj3}
}
```

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## References

37 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Emily Allaway and Kathleen McKeown. 2020a. Zero-shot stance detection: A dataset and model using generalized topic representations. arXiv preprint arXiv:2010.03640. [find](https://scholar.google.com/scholar?q=Emily+Allaway+and+Kathleen+McKeown%2E+2020a%2E+Zero%2Dshot+stance+detection%3A+A+dataset+and+model+using+generalized+topic+representations%2E+arXiv+preprint+arXiv%3A2010%2E03640%2E) [our card](https://md.research.elamur.ai/free/papers/allaway-2020-zero-shot-stance-detection-dataset/)
2. Emily Allaway and Kathleen R. McKeown. 2020b. Zero-shot stance detection: A dataset and model using generalized topic representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 8913–8931. Association for… [find](https://scholar.google.com/scholar?q=Emily+Allaway+and+Kathleen+R%2E+McKeown%2E+2020b%2E+Zero%2Dshot+stance+detection%3A+A+dataset+and+model+using+generalized+topic+representations%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Nat) [our card](https://md.research.elamur.ai/free/papers/allaway-2020-zero-shot-stance-detection-dataset/)
3. I Augenstein, T Rocktaeschel, A Vlachos, and K Bontcheva. 2016. Stance detection with bidirectional conditional encoding. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Sheffield. [find](https://scholar.google.com/scholar?q=I+Augenstein%2C+T+Rocktaeschel%2C+A+Vlachos%2C+and+K+Bontcheva%2E+2016%2E+Stance+detection+with+bidirectional+conditional+encoding%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+Natural+Language+Proc) [our card](https://md.research.elamur.ai/free/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/)
4. Erik Cambria, Soujanya Poria, Devamanyu Haz-arika, and Kenneth Kwok. 2018. Senticnet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In Thirty-Second AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Erik+Cambria%2C+Soujanya+Poria%2C+Devamanyu+Haz%2Darika%2C+and+Kenneth+Kwok%2E+2018%2E+Senticnet+5%3A+Discovering+conceptual+primitives+for+sentiment+analysis+by+means+of+context+embeddings%2E+In+Thirty%2DSecond+AAAI+C)
5. Alessandra Teresa Cignarella, Cristina Bosco, and Paolo Rosso. 2022. Do dependency relations help in the task of stance detection? In Proceedings of the Third Workshop on Insights from Negative Results in NLP, Insights@ACL 2022, Dublin, Ireland, May 26, 2022, pages 10–17. Association for Computation… [find](https://scholar.google.com/scholar?q=Alessandra+Teresa+Cignarella%2C+Cristina+Bosco%2C+and+Paolo+Rosso%2E+2022%2E+Do+dependency+relations+help+in+the+task+of+stance+detection%3F+In+Proceedings+of+the+Third+Workshop+on+Insights+from+Negative+Result)
6. Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2020. Will-they-won’t-they: A very large dataset for stance detection on twitter. arXiv preprint arXiv:2005.00388. [find](https://scholar.google.com/scholar?q=Costanza+Conforti%2C+Jakob+Berndt%2C+Mohammad+Taher+Pilehvar%2C+Chryssi+Giannitsarou%2C+Flavio+Toxvaerd%2C+and+Nigel+Collier%2E+2020%2E+Will%2Dthey%2Dwon%E2%80%99t%2Dthey%3A+A+very+large+dataset+for+stance+detection+on+twitter%2E+ar) [our card](https://md.research.elamur.ai/free/papers/conforti-2020-will-they-won-t-they/)
7. Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2021. Synthetic examples improve cross-target generalization: A study on stance detection on a twitter corpus. In Proceedings of the Eleventh Workshop on Computational Approaches to Su… [find](https://scholar.google.com/scholar?q=Costanza+Conforti%2C+Jakob+Berndt%2C+Mohammad+Taher+Pilehvar%2C+Chryssi+Giannitsarou%2C+Flavio+Toxvaerd%2C+and+Nigel+Collier%2E+2021%2E+Synthetic+examples+improve+cross%2Dtarget+generalization%3A+A+study+on+stance+dete) [our card](https://md.research.elamur.ai/free/papers/conforti-2021-synthetic-examples-improve-cross-target/)
8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2019%2E+BERT%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+In+Proceedings+of+the+2019+Conference+of+the+N)
9. Kuntal Dey, Ritvik Shrivastava, and Saroj Kaushik. 2018. Topical stance detection for twitter: A two-phase lstm model using attention. In European Conference on Information Retrieval, pages 529– 536. Springer. [find](https://scholar.google.com/scholar?q=Kuntal+Dey%2C+Ritvik+Shrivastava%2C+and+Saroj+Kaushik%2E+2018%2E+Topical+stance+detection+for+twitter%3A+A+two%2Dphase+lstm+model+using+attention%2E+In+European+Conference+on+Information+Retrieval%2C+pages+529%E2%80%93+536%2E+)
10. Daijun Ding, Rong Chen, Liwen Jing, Bowen Zhang, Xu Huang, Li Dong, Xiaowen Zhao, and Ge Song. 2024. Cross-target stance detection by exploiting target analytical perspectives. arXiv preprint arXiv:2401.01761. — [arXiv:2401.01761](https://arxiv.org/abs/2401.01761) [find](https://scholar.google.com/scholar?q=Daijun+Ding%2C+Rong+Chen%2C+Liwen+Jing%2C+Bowen+Zhang%2C+Xu+Huang%2C+Li+Dong%2C+Xiaowen+Zhao%2C+and+Ge+Song%2E+2024%2E+Cross%2Dtarget+stance+detection+by+exploiting+target+analytical+perspectives%2E+arXiv+preprint+arXiv%3A24)
11. Jiachen Du, Ruifeng Xu, Yulan He, and Lin Gui. 2017a. Stance classification with target-specific neural attention. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pages 3988–3994. [find](https://scholar.google.com/scholar?q=Jiachen+Du%2C+Ruifeng+Xu%2C+Yulan+He%2C+and+Lin+Gui%2E+2017a%2E+Stance+classification+with+target%2Dspecific+neural+attention%2E+In+Proceedings+of+the+Twenty%2DSixth+International+Joint+Conference+on+Artificial+Intel)
12. Jiachen Du, Ruifeng Xu, Yulan He, and Lin Gui. 2017b. Stance classification with target-specific neural attention networks. International Joint Conferences on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Jiachen+Du%2C+Ruifeng+Xu%2C+Yulan+He%2C+and+Lin+Gui%2E+2017b%2E+Stance+classification+with+target%2Dspecific+neural+attention+networks%2E+International+Joint+Conferences+on+Artificial+Intelligence%2E)
13. Kyle Glandt, Sarthak Khanal, Yingjie Li, Doina Caragea, and Cornelia Caragea. 2021. Stance detection in covid-19 tweets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Long Papers)… [find](https://scholar.google.com/scholar?q=Kyle+Glandt%2C+Sarthak+Khanal%2C+Yingjie+Li%2C+Doina+Caragea%2C+and+Cornelia+Caragea%2E+2021%2E+Stance+detection+in+covid%2D19+tweets%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+) [our card](https://md.research.elamur.ai/free/papers/glandt-2021-stance-detection-covid-19-tweets/)
14. Hu Huang, Bowen Zhang, Yangyang Li, Baoquan Zhang, Yuxi Sun, Chuyao Luo, and Cheng Peng. 2023. Knowledge-enhanced prompt-tuning for stance detection. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(6):1–20. [find](https://scholar.google.com/scholar?q=Hu+Huang%2C+Bowen+Zhang%2C+Yangyang+Li%2C+Baoquan+Zhang%2C+Yuxi+Sun%2C+Chuyao+Luo%2C+and+Cheng+Peng%2E+2023%2E+Knowledge%2Denhanced+prompt%2Dtuning+for+stance+detection%2E+ACM+Transactions+on+Asian+and+Low%2DResource+Languag)
15. Rachna Jain, Deepak Kumar Jain, Dharana, and Nitika Sharma. 2022. Fake news classification: A quantitative research description. ACM Trans. Asian Low Resour. Lang. Inf. Process., 21(1):3:1– 3:17. of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2355–2365. [find](https://scholar.google.com/scholar?q=Rachna+Jain%2C+Deepak+Kumar+Jain%2C+Dharana%2C+and+Nitika+Sharma%2E+2022%2E+Fake+news+classification%3A+A+quantitative+research+description%2E+ACM+Trans%2E+Asian+Low+Resour%2E+Lang%2E+Inf%2E+Process%2E%2C+21%281%29%3A3%3A1%E2%80%93+3%3A17%2E+of+t)
16. Yingjie Li, Chenye Zhao, and Cornelia Caragea. 2023b. Tts: A target-based teacher-student framework for zero-shot stance detection. In Proceedings of the ACM Web Conference 2023, WWW ’23, page 1500–1509, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Yingjie+Li%2C+Chenye+Zhao%2C+and+Cornelia+Caragea%2E+2023b%2E+Tts%3A+A+target%2Dbased+teacher%2Dstudent+framework+for+zero%2Dshot+stance+detection%2E+In+Proceedings+of+the+ACM+Web+Conference+2023%2C+WWW+%E2%80%9923%2C+page+1500%E2%80%9315)
17. Yupeng Li, Haorui He, Shaonan Wang, Francis Lau, and Yunya Song. 2022b. Improved target-specific stance detection on social media platforms by delving into conversation threads. arXiv preprint arXiv:2211.03061. — [arXiv:2211.03061](https://arxiv.org/abs/2211.03061) [find](https://scholar.google.com/scholar?q=Yupeng+Li%2C+Haorui+He%2C+Shaonan+Wang%2C+Francis+Lau%2C+and+Yunya+Song%2E+2022b%2E+Improved+target%2Dspecific+stance+detection+on+social+media+platforms+by+delving+into+conversation+threads%2E+arXiv+preprint+arXiv%3A2)
18. Bin Liang, Zixiao Chen, Lin Gui, Yulan He, Min Yang, and Ruifeng Xu. 2022a. Zero-shot stance detection via contrastive learning. In Proceedings of the ACM Web Conference 2022, pages 2738– 2747. [find](https://scholar.google.com/scholar?q=Bin+Liang%2C+Zixiao+Chen%2C+Lin+Gui%2C+Yulan+He%2C+Min+Yang%2C+and+Ruifeng+Xu%2E+2022a%2E+Zero%2Dshot+stance+detection+via+contrastive+learning%2E+In+Proceedings+of+the+ACM+Web+Conference+2022%2C+pages+2738%E2%80%93+2747%2E)
19. Bin Liang, Qinlin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, and Ruifeng Xu. 2022b. Jointcl: a joint contrastive learning framework for zero-shot stance detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 81–… [find](https://scholar.google.com/scholar?q=Bin+Liang%2C+Qinlin+Zhu%2C+Xiang+Li%2C+Min+Yang%2C+Lin+Gui%2C+Yulan+He%2C+and+Ruifeng+Xu%2E+2022b%2E+Jointcl%3A+a+joint+contrastive+learning+framework+for+zero%2Dshot+stance+detection%2E+In+Proceedings+of+the+60th+Annual+M) [our card](https://md.research.elamur.ai/free/papers/liang-2022-jointcl-joint-contrastive-learning-framework/)
20. Rui Liu, Zheng Lin, Yutong Tan, and Weiping Wang. 2021. Enhancing zero-shot and few-shot stance detection with commonsense knowledge graph. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3152– 3157. [find](https://scholar.google.com/scholar?q=Rui+Liu%2C+Zheng+Lin%2C+Yutong+Tan%2C+and+Weiping+Wang%2E+2021%2E+Enhancing+zero%2Dshot+and+few%2Dshot+stance+detection+with+commonsense+knowledge+graph%2E+In+Findings+of+the+Association+for+Computational+Linguistics) [our card](https://md.research.elamur.ai/free/papers/liu-2021-enhancing-zero-shot-few-shot/)
21. Mary L McHugh. 2012. Interrater reliability: the kappa statistic. Biochemia medica, 22(3):276– 282. [find](https://scholar.google.com/scholar?q=Mary+L+McHugh%2E+2012%2E+Interrater+reliability%3A+the+kappa+statistic%2E+Biochemia+medica%2C+22%283%29%3A276%E2%80%93+282%2E)
22. Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882. [find](https://scholar.google.com/scholar?q=Yoon+Kim%2E+2014%2E+Convolutional+neural+networks+for+sentence+classification%2E+arXiv+preprint+arXiv%3A1408%2E5882%2E) [our card](https://md.research.elamur.ai/free/papers/kim-2014-convolutional-neural-networks-sentence-classification/)
23. Ang Li, Bin Liang, Jingqian Zhao, Bowen Zhang, Min Yang, and Ruifeng Xu. 2023a. Stance detection on social media with background knowledge. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15703–15717, Singapore. Association for Computational Linguisti… [find](https://scholar.google.com/scholar?q=Ang+Li%2C+Bin+Liang%2C+Jingqian+Zhao%2C+Bowen+Zhang%2C+Min+Yang%2C+and+Ruifeng+Xu%2E+2023a%2E+Stance+detection+on+social+media+with+background+knowledge%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+i) [our card](https://md.research.elamur.ai/free/papers/li-2023-stance-detection-social-media-background/)
24. Chen Li, Hao Peng, Jianxin Li, Lichao Sun, Lingjuan Lyu, Lihong Wang, Philip S. Yu, and Lifang He. 2022a. Joint stance and rumor detection in hierarchical heterogeneous graph. IEEE Trans. Neural Networks Learn. Syst., 33(6):2530– 2542. [find](https://scholar.google.com/scholar?q=Chen+Li%2C+Hao+Peng%2C+Jianxin+Li%2C+Lichao+Sun%2C+Lingjuan+Lyu%2C+Lihong+Wang%2C+Philip+S%2E+Yu%2C+and+Lifang+He%2E+2022a%2E+Joint+stance+and+rumor+detection+in+hierarchical+heterogeneous+graph%2E+IEEE+Trans%2E+Neural+Netwo)
25. Yingjie Li, Tiberiu Sosea, Aditya Sawant, Ajith Ja-yaraman Nair, Diana Inkpen, and Cornelia Caragea. 2021. P-stance: A large dataset for stance detection in political domain. In Findings Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiao-Dan Zhu, and Colin Cherry. 2016. Semeval-2016 task 6:… [find](https://scholar.google.com/scholar?q=Yingjie+Li%2C+Tiberiu+Sosea%2C+Aditya+Sawant%2C+Ajith+Ja%2Dyaraman+Nair%2C+Diana+Inkpen%2C+and+Cornelia+Caragea%2E+2021%2E+P%2Dstance%3A+A+large+dataset+for+stance+detection+in+political+domain%2E+In+Findings+Saif+Mohammad) [our card](https://md.research.elamur.ai/free/papers/li-2021-p-stance-large-dataset-stance/)
26. Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Stance and sentiment in tweets. ACM Trans. Internet Technol., 17(3). [find](https://scholar.google.com/scholar?q=Saif+M%2E+Mohammad%2C+Parinaz+Sobhani%2C+and+Svetlana+Kiritchenko%2E+2017%2E+Stance+and+sentiment+in+tweets%2E+ACM+Trans%2E+Internet+Technol%2E%2C+17%283%29%2E)
27. Sujata Rani and Parteek Kumar. 2022. Aspect-based sentiment analysis using dependency parsing. ACM Trans. Asian Low Resour. Lang. Inf. Process., 21(3):56:1–56:19. [find](https://scholar.google.com/scholar?q=Sujata+Rani+and+Parteek+Kumar%2E+2022%2E+Aspect%2Dbased+sentiment+analysis+using+dependency+parsing%2E+ACM+Trans%2E+Asian+Low+Resour%2E+Lang%2E+Inf%2E+Process%2E%2C+21%283%29%3A56%3A1%E2%80%9356%3A19%2E)
28. M. Schuster and K.K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681. [find](https://scholar.google.com/scholar?q=M%2E+Schuster+and+K%2EK%2E+Paliwal%2E+1997%2E+Bidirectional+recurrent+neural+networks%2E+IEEE+Transactions+on+Signal+Processing%2C+45%2811%29%3A2673%E2%80%932681%2E)
29. Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980. [find](https://scholar.google.com/scholar?q=Taylor+Shin%2C+Yasaman+Razeghi%2C+Robert+L+Logan+IV%2C+Eric+Wallace%2C+and+Sameer+Singh%2E+2020%2E+Autoprompt%3A+Eliciting+knowledge+from+language+models+with+automatically+generated+prompts%2E+arXiv+preprint+arXiv%3A2) [our card](https://md.research.elamur.ai/free/papers/shin-2020-autoprompt-eliciting-knowledge-language-models/)
30. Sainbayar Sukhbaatar, arthur szlam, Jason Weston, and Rob Fergus. 2015. End-to-end memory networks. In Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc. [find](https://scholar.google.com/scholar?q=Sainbayar+Sukhbaatar%2C+arthur+szlam%2C+Jason+Weston%2C+and+Rob+Fergus%2E+2015%2E+End%2Dto%2Dend+memory+networks%2E+In+Advances+in+Neural+Information+Processing+Systems%2C+volume+28%2E+Curran+Associates%2C+Inc%2E)
31. Qingying Sun, Zhongqing Wang, Qiaoming Zhu, and Guodong Zhou. 2018. Stance detection with hierarchical attention network. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2399–2409. [find](https://scholar.google.com/scholar?q=Qingying+Sun%2C+Zhongqing+Wang%2C+Qiaoming+Zhu%2C+and+Guodong+Zhou%2E+2018%2E+Stance+detection+with+hierarchical+attention+network%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Linguisti) [our card](https://md.research.elamur.ai/free/papers/sun-2018-stance-detection-hierarchical-attention-network/)
32. Ramon Villa-Cox, Sumeet Kumar, Matthew Bab-cock, and Kathleen M Carley. 2020. Stance in replies and quotes (srq): A new dataset for learning stance in twitter conversations. arXiv preprint arXiv:2006.00691. — [arXiv:2006.00691](https://arxiv.org/abs/2006.00691) [find](https://scholar.google.com/scholar?q=Ramon+Villa%2DCox%2C+Sumeet+Kumar%2C+Matthew+Bab%2Dcock%2C+and+Kathleen+M+Carley%2E+2020%2E+Stance+in+replies+and+quotes+%28srq%29%3A+A+new+dataset+for+learning+stance+in+twitter+conversations%2E+arXiv+preprint+arXiv%3A2006%2E)
33. Penghui Wei, Junjie Lin, and Wenji Mao. 2018. Multi-target stance detection via a dynamic memory-augmented network. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 1229–1232. ACM. [find](https://scholar.google.com/scholar?q=Penghui+Wei%2C+Junjie+Lin%2C+and+Wenji+Mao%2E+2018%2E+Multi%2Dtarget+stance+detection+via+a+dynamic+memory%2Daugmented+network%2E+In+The+41st+International+ACM+SIGIR+Conference+on+Research+%26+Development+in+Informat)
34. Penghui Wei and Wenji Mao. 2019. Modeling transferable topics for cross-target stance detection. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1173–1176. ACM. [find](https://scholar.google.com/scholar?q=Penghui+Wei+and+Wenji+Mao%2E+2019%2E+Modeling+transferable+topics+for+cross%2Dtarget+stance+detection%2E+In+Proceedings+of+the+42nd+International+ACM+SIGIR+Conference+on+Research+and+Development+in+Informatio)
35. Wei Xue and Tao Li. 2018. Aspect based sentiment analysis with gated convolutional networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2514–2523, Melbourne, Australia. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Wei+Xue+and+Tao+Li%2E+2018%2E+Aspect+based+sentiment+analysis+with+gated+convolutional+networks%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+)
36. Bowen Zhang, Daijun Ding, Guangning Xu, Jinjin Guo, Zhichao Huang, and Xu Huang. 2023. Twitter stance detection via neural production systems. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE. [find](https://scholar.google.com/scholar?q=Bowen+Zhang%2C+Daijun+Ding%2C+Guangning+Xu%2C+Jinjin+Guo%2C+Zhichao+Huang%2C+and+Xu+Huang%2E+2023%2E+Twitter+stance+detection+via+neural+production+systems%2E+In+ICASSP+2023%2D2023+IEEE+International+Conference+on+Acou)
37. Bowen Zhang, Min Yang, Xutao Li, Yunming Ye, Xiaofei Xu, and Kuai Dai. 2020. Enhancing cross-target stance detection with transferable semantic-emotion knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3188–3197. [find](https://scholar.google.com/scholar?q=Bowen+Zhang%2C+Min+Yang%2C+Xutao+Li%2C+Yunming+Ye%2C+Xiaofei+Xu%2C+and+Kuai+Dai%2E+2020%2E+Enhancing+cross%2Dtarget+stance+detection+with+transferable+semantic%2Demotion+knowledge%2E+In+Proceedings+of+the+58th+Annual+Mee) [our card](https://md.research.elamur.ai/free/papers/zhang-2020-enhancing-cross-target-stance-detection/)