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Stance Detection: Concepts, Approaches, Resources, and Outstanding Issues

Published: 11 July 2021 Publication History

Abstract

Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. The output of the stance detection procedure is usually from this set: Favor, Against, None. In this tutorial, we will define the core concepts and research problems related to stance detection, present historical and contemporary approaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and application areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon.

References

[1]
Aseel Addawood, Jodi Schneider, and Masooda Bashir. 2017. Stance classification of Twitter debates: the encryption debate as a use case. In Proceedings of the 8th International Conference on Social Media & Society. 2.
[2]
Rabab Alkhalifa and Arkaitz Zubiaga. 2020. QMUL-SDS at SardiStance2020: Leveraging network interactions to boost performance on stance detection using knowledge graphs. arXiv preprint arXiv:2011.01181 (2020).
[3]
Emily Allaway, Malavika Srikanth, and Kathleen McKeown. 2021. Adversarial learning for zero-shot stance detection on social media. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics .
[4]
Pranav Anand, Marilyn Walker, Rob Abbott, Jean E Fox Tree, Robeson Bowmani, and Michael Minor. 2011. Cats rule and dogs drool!: classifying stance in online debate. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis. 1--9.
[5]
Hamed Bonab and Fazli Can. 2019. Less is more: a comprehensive framework for the number of components of ensemble classifiers. IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, 9 (2019), 2735--2745.
[6]
Hamed R Bonab and Fazli Can. 2018. GOOWE: geometrically optimum and online-weighted ensemble classifier for evolving data streams. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 12, 2 (2018), 25.
[7]
Umit Can and Bilal Alatas. 2021. A novel approach for efficient stance detection in online social networks with metaheuristic optimization. Technology in Society, Vol. 64 (2021), 101501.
[8]
Alessandra Teresa Cignarella, Mirko Lai, Cristina Bosco, Viviana Patti, Rosso Paolo, et al. 2020. SardiStance@EVALITA2020: Overview of the task on stance detection in Italian tweets. In EVALITA 2020 Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. 1--10.
[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. 529--536.
[10]
Shiri Dori-Hacohen. 2015. Controversy detection and stance analysis. In Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval. 1057.
[11]
Adam Faulkner. 2014. Automated classification of stance in student essays: an approach using stance target information and the Wikipedia link-based measure. In Proceedings of the International Florida Artificial Intelligence Research Society Conference. 174--179.
[12]
Ronen Feldman. 2013. Techniques and applications for sentiment analysis. Commun. ACM, Vol. 56, 4 (2013), 82--89.
[13]
Kazi Saidul Hasan and Vincent Ng. 2013. Stance classification of ideological debates: data, models, features, and constraints. In Proceedings of the International Joint Conference on Natural Language Processing. 1348--1356.
[14]
Tomávs Hercig, Peter Krejzl, Barbora Hourová, Josef Steinberger, and Ladislav Lenc. 2017. Detecting stance in Czech news commentaries. In Proceedings of the Conference on Theory and Practice of Information Technologies (ITAT) .
[15]
Aditya Joshi, Pushpak Bhattacharyya, and Mark J Carman. 2017. Automatic sarcasm detection: a survey. ACM Computing Surveys (CSUR), Vol. 50, 5 (2017), 73.
[16]
Dilek Küccük. 2017. Stance detection in Turkish tweets. In Proceedings of the International Workshop on Social Media World Sensors .
[17]
Dilek Küccük and Fazli Can. 2018. Stance detection on tweets: an SVM-based approach. arXiv preprint arXiv:1803.08910 (2018).
[18]
Dilek Kücc ük and Fazli Can. 2019. A tweet dataset annotated for named entity recognition and stance detection. arXiv preprint arXiv:1901.04787 (2019).
[19]
Dilek Kücc ük and Fazli Can. 2020. Stance detection: A survey. ACM Computing Surveys (CSUR), Vol. 53, 1 (2020), 1--37. https://doi.org/10.1145/3369026
[20]
Mirko Lai, Alessandra Teresa Cignarella, Delia Irazú Hernández Far'ias, Cristina Bosco, Viviana Patti, and Paolo Rosso. 2020. Multilingual stance detection in social media political debates. Computer Speech & Language (2020), 101075.
[21]
Mirko Lai, Viviana Patti, Giancarlo Ruffo, and Paolo Rosso. 2018. Stance evolution and Twitter interactions in an Italian political debate. In Proceedings of 23rd International Conference on Natural Language and Information Systems .
[22]
David MJ Lazer, Matthew A Baum, Yochai Benkler, Adam J Berinsky, Kelly M Greenhill, Filippo Menczer, Miriam J Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, et al. 2018. The science of fake news. Science, Vol. 359, 6380 (2018), 1094--1096.
[23]
Marco Lippi and Paolo Torroni. 2016. Argumentation mining: state of the art and emerging trends. ACM Transactions on Internet Technology (TOIT), Vol. 16, 2 (2016), 10.
[24]
Can Liu, Wen Li, Bradford Demarest, Yue Chen, Sara Couture, Daniel Dakota, Nikita Haduong, Noah Kaufman, Andrew Lamont, Manan Pancholi, Kenneth Steimel, and Sandra Kübler. 2016. IUCL at SemEval-2016 task 6: an ensemble model for stance detection in Twitter. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 394--400.
[25]
Saif M Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016a. A dataset for detecting stance in tweets. In Proceedings of the Language Resources and Evaluation Conference. 3945--3952.
[26]
Saif M Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016b. SemEval-2016 task 6: detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 31--41.
[27]
Saif M Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Stance and sentiment in tweets. ACM Transactions on Internet Technology, Vol. 17, 3 (2017), Article 26.
[28]
Mauridhi Hery Purnomo, Surya Sumpeno, Esther Irawati Setiawan, and Diana Purwitasari. 2017. Biomedical engineering research in the social network analysis era: stance classification for analysis of hoax medical news in social media. Procedia Computer Science, Vol. 116 (2017), 3--9.
[29]
Ashwin Rajadesingan and Huan Liu. 2014. Identifying users with opposing opinions in Twitter debates. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. 153--160.
[30]
Kashfia Sailunaz, Manmeet Dhaliwal, Jon Rokne, and Reda Alhajj. 2018. Emotion detection from text and speech: a survey. Social Network Analysis and Mining, Vol. 8, 1 (2018), 28.
[31]
Benjamin Schiller, Johannes Daxenberger, and Iryna Gurevych. 2021. Stance detection benchmark: How robust is your stance detection? KI-Künstliche Intelligenz (2021), 1--13.
[32]
Anirban Sen, Manjira Sinha, Sandya Mannarswamy, and Shourya Roy. 2018. Stance classification of multi-perspective consumer health information. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. 273--281.
[33]
Parinaz Sobhani. 2017. Stance detection and analysis in social media. Ph.D. Dissertation. Université d'Ottawa/University of Ottawa.
[34]
Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu. 2017. A dataset for multi-target stance detection. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics. 551--557.
[35]
Qingying Sun, Zhongqing Wang, Shoushan Li, Qiaoming Zhu, and Guodong Zhou. 2018a. Stance detection via sentiment information and neural network model. Frontiers of Computer Science (2018).
[36]
Qingying Sun, Zhongqing Wang, Qiaoming Zhu, and Guodong Zhou. 2018b. Stance detection with hierarchical attention network. In Proceedings of the International Conference on Computational Linguistics. 2399--2409.
[37]
Sahil Swami, Ankush Khandelwal, Vinay Singh, Syed Sarfaraz Akhtar, and Manish Shrivastava. 2018. An English-Hindi code-mixed corpus: stance annotation and baseline system. arXiv preprint arXiv:1805.11868 (2018).
[38]
Mariona Taulé, M Antonia Mart'i, Francisco Rangel, Paolo Rosso, Cristina Bosco, and Viviana Patti. 2017. Overview of the task on stance and gender detection in tweets on Catalan independence at IberEval 2017. In Proceedings of the Second Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2017) .
[39]
Matt Thomas, Bo Pang, and Lillian Lee. 2006. Get out the vote: determining support or opposition from congressional floor-debate transcripts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 327--335.
[40]
Martin Tutek, Ivan Sekulic, Paula Gombar, Ivan Paljak, Filip Culinovic, Filip Boltuzic, Mladen Karan, Domagoj Alagić, and Jan vS najder. 2016. Takelab at SemEval-2016 task 6: stance classification in tweets using a genetic algorithm based ensemble. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 464--468.
[41]
Jannis Vamvas and Rico Sennrich. 2020. X-stance: A multilingual multi-target dataset for stance detection. arXiv preprint arXiv:2003.08385 (2020).
[42]
Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, and Deb Roy. 2016. DeepStance at SemEval-2016 task 6: detecting stance in tweets using character and word-level CNNs. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) .
[43]
Byron C Wallace. 2015. Computational irony: a survey and new perspectives. Artificial Intelligence Review, Vol. 43, 4 (2015), 467--483.
[44]
Penghui Wei, Junjie Lin, and Wenji Mao. 2018. Multi-target stance detection via a dynamic memory-augmented network. In Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval. 1229--1232.
[45]
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. 1173--1176.
[46]
Wan Wei, Xiao Zhang, Xuqin Liu, Wei Chen, and Tengjiao Wang. 2016. pkudblab at SemEval-2016 task 6: a specific convolutional neural network system for effective stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 384--388.
[47]
Michael Wojatzki and Torsten Zesch. 2016. ltl.uni-due at SemEval-2016 task 6: stance detection in social media using stacked classifiers. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 428--433.
[48]
Chang Xu, Cecile Paris, Surya Nepal, and Ross Sparks. 2018. Cross-target stance classification with self-attention networks. arXiv preprint arXiv:1805.06593 (2018).
[49]
Ruifeng Xu, Yu Zhou, Dongyin Wu, Lin Gui, Jiachen Du, and Yun Xue. 2016. Overview of NLPCC shared task 4: stance detection in Chinese microblogs. In Natural Language Understanding and Intelligent Applications. 907--916.
[50]
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. 3188--3197.
[51]
Shaodian Zhang, Lin Qiu, Frank Chen, Weinan Zhang, Yong Yu, and Noémie Elhadad. 2017. We make choices we think are going to save us: debate and stance identification for online breast cancer CAM discussions. In Proceedings of the International Conference on World Wide Web Companion. 1073--1081.
[52]
Zhihua Zhang and Man Lan. 2016. ECNU at SemEval 2016 task 6: relevant or not? supportive or not? a two-step learning system for automatic detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 451--457.
[53]
Yiwei Zhou, Alexandra I Cristea, and Lei Shi. 2017. Connecting targets to tweets: semantic attention-based model for target-specific stance detection. In Proceedings of the International Conference on Web Information Systems Engineering. 18--32.
[54]
Lixing Zhu, Yulan He, and Deyu Zhou. 2020. Neural opinion dynamics model for the prediction of user-level stance dynamics. Information Processing & Management, Vol. 57, 2 (2020), 102031.
[55]
Elena Zotova, Rodrigo Agerri, and German Rigau. 2021. Semi-automatic generation of multilingual datasets for stance detection in Twitter. Expert Systems with Applications, Vol. 170 (2021), 114547.
[56]
Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. 2018. Detection and resolution of rumours in social media: a survey. ACM Computing Surveys (CSUR), Vol. 51, 2 (2018), 32.

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cover image ACM Conferences
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2021
2998 pages
ISBN:9781450380379
DOI:10.1145/3404835
© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 11 July 2021

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  1. data streams
  2. deep learning
  3. social media analysis
  4. stance detection

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  • (2024)Quantifying Variations in Controversial Discussions within Kuwaiti Social NetworksBig Data and Cognitive Computing10.3390/bdcc80600608:6(60)Online publication date: 4-Jun-2024
  • (2024)Dual Bi-LSTM-GRU based stance detection in tweets ordered classesNeural Computing and Applications10.1007/s00521-024-10549-9Online publication date: 6-Dec-2024
  • (2023)An Explainable Fake News Analysis Method with Stance InformationElectronics10.3390/electronics1215336712:15(3367)Online publication date: 7-Aug-2023
  • (2022)Enhancing Zero-Shot Stance Detection via Targeted Background KnowledgeProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531807(2070-2075)Online publication date: 6-Jul-2022

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