The page lists recent research developments in the area of Stance, Argumentation and Ideology (in political sense) Learning. This page includes a) Data repository b) Recent Publications c) Tutorials d) Other Useful Websites e) Related Work, but not necessarily on Stance
If you are new to this field, the paper titled "Stance and Sentiment in Tweets" by Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko, 2017 is a good place to start. If intereseted in argumentation, the paper titled "Five Years of Argument Mining: a Data-driven Analysis" is a good start.
If you would like me to add your (published) paper here, please email.
Table of content:
2020, Conforti, C., Berndt, J., Pilehvar, M. T., Giannitsarou, C., Toxvaerd, F., & Collier, N. (2020, July). Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 1715-1724).
2019, Küçük, D. and Can, F., 2019. A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection. arXiv preprint arXiv:1901.04787.
Pheme Stance dataset: https://figshare.com/articles/PHEME_rumour_scheme_dataset_journalism_use_case/2068650 2018, Benton, A. and Dredze, M., 2018. Using Author Embeddings to Improve Tweet Stance Classification. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text (pp. 184-194).
2018, Yoshioka, Masaharu, Myungha Jang, James Allan and Noriko Kando. “Visualizing Polarity-based Stances of News Websites.” NewsIR@ECIR (2018). http://ceur-ws.org/Vol-2079/paper2.pdf
2017, Johnson, K., Lee, I.T. and Goldwasser, D., 2017, August. Ideological phrase indicators for classification of political discourse framing on Twitter. In Proceedings of the Second Workshop on NLP and Computational Social Science (pp. 90-99).
2017, Simaki, Vasiliki, Carita Paradis, Maria Skeppstedt, Magnus Sahlgren, Kostiantyn Kucher, and Andreas Kerren. "Annotating speaker stance in discourse: the Brexit Blog Corpus." Corpus Linguistics and Linguistic Theory (2017).
2017, Sobhani, Parinaz, Diana Inkpen, and Xiaodan Zhu. "A dataset for multi-target stance detection." In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, vol. 2, pp. 551-557. 2017.
2016, Task 6: Detecting Stance in Tweets http://alt.qcri.org/semeval2016/task6/
2016, Toledo-Ronen, Orith, Roy Bar-Haim, and Noam Slonim. "Expert stance graphs for computational argumentation." In Proceedings of the Third Workshop on Argument Mining (ArgMining2016), pp. 119-123. 2016.
2016, Ferreira, W., & Vlachos, A. (2016). Emergent: a novel data-set for stance classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 1163-1168).
Abeer Aldayel and Walid Magdy. 2019. Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 205 (November 2019), 20 pages. DOI: https://doi.org/10.1145/3359307
2019, Ma, Weizhi, Zhen Wang, Min Zhang, Jing Qian, Huanbo Luan, Yiqun Liu, and Shaoping Ma. "Stance Influences Your Thoughts: Psychology-Inspired Social Media Analytics. " In CCF International Conference on Natural Language Processing and Chinese Computing, pp. 685-697. Springer, Cham, 2019.
2019, Sumeet Kumar, and Kathleen M. Carley Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5047–5058, Florence, Italy, July 28 - August 2, 2019.
2019, Esin Durmus, Faisal Ladhak, and Claire Cardie Determining Relative Argument Specificity and Stancefor Complex Argumentative Structures, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4630–4641 Florence, Italy, July 28 - August 2, 2019
2019 Chang Xu, Cecile Paris, Surya Nepal,and Ross Sparks, Recognising Agreement and Disagreement between Stances with Reason Comparing Networks,Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4665–4671, Florence, Italy, July 28 - August 2, 2019.
2019 Zhaohao Zeng, Ruihua Song, Pingping Lin, and Tetsuya Sakai. 2019. Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19). ACM, New York, NY, USA, 285-293. DOI: https://doi.org/10.1145/3289600.3291038
2019, Darwish, K., Stefanov, P., Aupetit, M.J. and Nakov, P., 2019. Unsupervised User Stance Detection on Twitter. arXiv preprint arXiv:1904.02000.
2019, Qiang Zhang, Shangsong Liang, Aldo Lipani, Zhaochun Ren, and Emine Yilmaz. 2019. From Stances’ Imbalance to Their Hierarchical Representation and Detection. In Proceedings of the 2019 World Wide Web Conference (WWW ’19), May 13–17, 2019, San Francisco, CA, USA. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3308558.3313724
2018, Endang Wahyu Pamungkas, Valerio Basile, and Viviana Patti. 2018. Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure. In Proceedings of 2nd International Workshop on Rumours and Deception in Social Media (RDSM) co-located with International Conference on Information and Knowledge Management (RDSM, CIKM’18). ACM, New York, NY, USA http://static.aixpaper.com/pdf/6/af/arxiv.1901.01911.v1.pdf
2018, 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 (SIGIR '18). ACM, New York, NY, USA, 1229-1232. DOI: https://doi.org/10.1145/3209978.3210145
2018, Myungha Jang, James Allan, Explaining Controversy on Social Media via Stance Summarization, SIGIR, https://people.cs.umass.edu/~mhjang/papers/ExplainControversySIGIR18.pdf
2018, Cabrio, Elena, and Serena Villata. "Five Years of Argument Mining: a Data-driven Analysis." In IJCAI, pp. 5427-5433. 2018. https://www.ijcai.org/proceedings/2018/0766.pdf
2018, Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets Oana Cocarascu and Francesca Toni, Computational Linguistics 2018 Vol. 44, 833-858
2018, Wei, Penghui, Junjie Lin, and Wenji Mao. "Multi-Target Stance Detection via a Dynamic Memory-Augmented Network." In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1229-1232. ACM, 2018.
2018, Xu, Chang, Cecile Paris, Surya Nepal, and Ross Sparks. "Cross-Target Stance Classification with Self-Attention Networks." arXiv preprint arXiv:1805.06593 (2018), ACL 2108
2018, Myungha Jang, James Allan, Explaining Controversy on Social Media via Stance Summarization, https://people.cs.umass.edu/~mhjang/papers/ExplainControversySIGIR18.pdf
2018, Lahoti, Preethi, Kiran Garimella, and Aristides Gionis. "Joint non-negative matrix factorization for learning ideological leaning on Twitter." In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 351-359. ACM, 2018. https://arxiv.org/pdf/1711.10251
2017, Ebrahimi, J., Dou, D. and Lowd, D., 2016. Weakly supervised tweet stance classification by relational bootstrapping. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 1012-1017).
2017, Li, C., Guo, X. and Mei, Q., 2017, February. Deep memory networks for attitude identification. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (pp. 671-680). ACM.
2017, Joseph, K., Friedland, L., Hobbs, W., Lazar, D. & Tsur, O. (2017). Constance: Modeling Annotation Contexts to Improve Stance Classification. EMNLP 2017. https://kennyjoseph.github.io/papers/emnlp2017.pdf
2017, Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Stance and Sentiment in Tweets. ACM Trans. Internet Technol. 17, 3, Article 26 (June 2017), 23 pages. DOI: https://doi.org/10.1145/3003433
2017, Dong, Rui, Yizhou Sun, Lu Wang, Yupeng Gu, and Yuan Zhong. "Weakly-Guided User Stance Prediction via Joint Modeling of Content and Social Interaction." In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1249-1258. ACM, 2017. http://web.cs.ucla.edu/~yzsun/papers/2017_cikm_stance.pdf
2017, Gadek, Guillaume, Josefin Betsholtz, Alexandre Pauchet, Stéphan Brunessaux, Nicolas Malandain, and Laurent Vercouter. "Extracting Contextonyms from Twitter for Stance Detection." In ICAART (2), pp. 132-141. 2017. http://www.scitepress.org/Papers/2017/61909/61909.pdf
2017, Dey, Kuntal, Ritvik Shrivastava, and Saroj Kaushik. "Twitter stance detection-a subjectivity and sentiment polarity inspired two-phase approach." In SENTIRE Workshop, ICDM. 2017. http://sentic.net/sentire2017dey.pdf
2017, Shenoy, Gourav G., Erika H. Dsouza, and Sandra Kübler. "Performing Stance Detection on Twitter Data using Computational Linguistics Techniques." arXiv preprint arXiv:1703.02019 (2017). https://arxiv.org/pdf/1703.02019
2017, Vilares, David, and Yulan He. "Detecting Perspectives in Political Debates." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1573-1582. 2017. http://www.aclweb.org/anthology/D17-1165
A. Sasaki, J. Mizuno, N. Okazaki and K. Inui, "Stance Classification by Recognizing Related Events about Targets," 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Omaha, NE, 2016, pp. 582-587.
2016, Misra, Amita, Brian Ecker, Theodore Handleman, Nicolas Hahn, and Marilyn Walker. "NLDS-UCSC at SemEval-2016 task 6: a semi-supervised approach to detecting stance in tweets." In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 420-427. 2016.
2016, Zarrella, Guido, and Amy Marsh. "MITRE at semeval-2016 task 6: Transfer learning for stance detection." arXiv preprint arXiv:1606.037
2016, Wei, Wan, Xiao Zhang, Xuqin Liu, Wei Chen, and Tengjiao Wang. "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), pp. 384-388. 2016.
2016, Zubiaga, Arkaitz, Elena Kochkina, Maria Liakata, Rob Procter, and Michal Lukasik. "Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations." arXiv preprint arXiv:1609.09028 (2016).
2016, Augenstein, Isabelle, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. "Stance detection with bidirectional conditional encoding." arXiv preprint arXiv:1606.05464 (2016).
2016, Gu, Yupeng, Ting Chen, Yizhou Sun, and Bingyu Wang. "Ideology Detection for Twitter Users with Heterogeneous Types of Links." arXiv preprint arXiv:1612.08207 (2016).
2016, Johnson, Kristen, and Dan Goldwasser. "" All I know about politics is what I read in Twitter": Weakly Supervised Models for Extracting Politicians’ Stances From Twitter." In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2966-2977. 2016
2016, Ebrahimi, Javid, Dejing Dou, and Daniel Lowd. "A joint sentiment-target-stance model for stance classification in tweets." In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2656-2665. 2016.
2016, Johnson, Kristen, and Dan Goldwasser. "Identifying stance by analyzing political discourse on twitter." In Proceedings of the First Workshop on NLP and Computational Social Science, pp. 66-75. 2016. http://www.aclweb.org/anthology/W16-5609
2016, Wojatzki, Michael, and Torsten Zesch. "Stance-based Argument Mining–Modeling Implicit Argumentation Using Stance." Proceedings of the KONVENS, Bochum, Germany (2016): 313-322. http://www.academia.edu/download/51214941/016-konvens2016.pdf#page=324
2015, Sobhani, Parinaz, Diana Inkpen, and Stan Matwin. "From argumentation mining to stance classification." In Proceedings of the 2nd Workshop on Argumentation Mining, pp. 67-77. 2015. http://www.aclweb.org/anthology/W15-0509
2015, Sridhar, Dhanya, James Foulds, Bert Huang, Lise Getoor, and Marilyn Walker. "Joint models of disagreement and stance in online debate." In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 116-125. 2015. http://www.aclweb.org/anthology/P15-1012
2015, Lu, Haokai, James Caverlee, and Wei Niu. "Biaswatch: A lightweight system for discovering and tracking topic-sensitive opinion bias in social media." In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 213-222. ACM, 2015.
2014, Gao H, Mahmud J, Chen J, Nichols J, Zhou M. Modeling user attitude toward controversial topics in online social media. InEighth International AAAI Conference on Weblogs and Social Media 2014 May 16.
2014, Akoglu, Leman. "Quantifying political polarity based on bipartite opinion networks." In Eighth International AAAI Conference on Weblogs and Social Media. 2014.
2014, Gu, Yupeng, Yizhou Sun, Ning Jiang, Bingyu Wang, and Ting Chen. "Topic-factorized ideal point estimation model for legislative voting network." In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 183-192. ACM, 2014.
2014, Hasan, Kazi Saidul, and Vincent Ng. "Why are you taking this stance? identifying and classifying reasons in ideological debates." In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 751-762. 2014. http://www.aclweb.org/anthology/D14-1083
2014, Sridhar, D., Getoor, L., & Walker, M. (2014). Collective stance classification of posts in online debate forums. In Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media (pp. 109-117).
2013, Gottipati, Swapna, Minghui Qiu, Liu Yang, Feida Zhu, and Jing Jiang. "Predicting user’s political party using ideological stances." In International Conference on Social Informatics, pp. 177-191. Springer, Cham, 2013.
2013, Hasan, K. S., & Ng, V. (2013). Extra-linguistic constraints on stance recognition in ideological debates. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Vol. 2, pp. 816-821).
2013, Hasan, K. S., & Ng, V. (2013). Stance classification of ideological debates: Data, models, features, and constraints. In Proceedings of the Sixth International Joint Conference on Natural Language Processing (pp. 1348-1356).
2011, Anand, Pranav, Marilyn Walker, Rob Abbott, Jean E. Fox Tree, Robeson Bowmani, and Michael Minor. "Cats rule and dogs drool!: Classifying stance in online debate." In Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, pp. 1-9. Association for Computational Linguistics, 2011. https://aclanthology.info/pdf/W/W11/W11-1701.pdf
2010, Somasundaran, Swapna, and Janyce Wiebe. "Recognizing stances in ideological on-line debates." In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116-124. Association for Computational Linguistics, 2010. https://aclanthology.info/pdf/W/W10/W10-0214.pdf
Task 6: Detecting Stance in Tweets http://alt.qcri.org/semeval2016/task6/