Abstract

Stance detection (also known as stance classification, stance prediction, and stance analysis) 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. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications 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 Web content including social media.

Tutorial Outline

1. Introduction
2. Core concepts and related problems
• Definitions of stance detection and subproblems of stance detection.
• Interrelationships with related problems including sentiment analysis, emotion recognition, sarcasm/irony detection, controversy detection, and argument mining
3. Stance detection competitions (shared tasks)
• SemEval-2016 Task 6: Detecting Stance in Tweets
• Shared Task of Stance Detection in Chinese Microblogs at NLPCC-ICCPOL-2016
• Shared Task of Stance Detection in Spanish and Catalan Tweets at IberEval-2017
• SardiStance: Stance Detection Task in Italian Tweets at EVALITA-2020
• VaxxStance: Going Beyond Text in Cross-lingual Stance Detection at IberLeF-2021
4. Historical and contemporary approaches
• Earlier studies on stance detection
• Machine learning based approaches
• Deep learning based approaches
• Ensemble learning approaches
• Related learning systems and tools
5. Common stance detection datasets
6. Application areas
• Opinion surveys/polling
• Public health surveillance
• Information retrieval
• Stance summarization
• Rumour classification
• Fake news detection
7. Outstanding issues
• Stance detection in data streams
• Cross-lingual and multilingual stance detection
• Context-sensitive stance detection
8. Conclusion

Related Publications

• Küçük, D., & Can, F. (2020). Stance detection: A survey. ACM Computing Surveys (CSUR), 53(1), 1-37.
• Küçük, D., & Can, F. (2021). Stance detection: Concepts, approaches, resources, and outstanding issues. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2673-2676).

Presenter Biographies

Dilek Küçük is an associate professor and chief researcher at the Energy Institute of TÜBİTAK Marmara Research Center (MRC) in Ankara, Turkey. She is also the leader of Power Systems Information Technologies Group at the institute. Her group is the recipient of the best research group award of TÜBİTAK MRC for the year 2017. She received her B.Sc., M.Sc. and Ph.D. degrees in Computer Engineering all from Middle East Technical University (Ankara, Turkey) in 2003, 2005, and 2011, respectively. Between May 2013 and May 2014, she studied as a post-doctoral researcher at European Commission's Joint Research Centre in Italy. Her research interests include stance detection, social media analysis, natural language processing, energy informatics, and data mining. She is the author or co-author of 16 papers published in SCI-indexed journals (including ACM-CSUR and IEEE transactions) and 42 papers presented at international conferences/workshops. She was a joint tutorial presenter at the SIGIR 2021 conference. Her personal Web page is available at https://dkucuk.github.io/en.html.

Fazli Can

Fazli Can received the B.S. and M.S. degrees in electrical and electronics and computer engineering and the Ph.D. degree in computer engineering from Middle East Technical University, Ankara, Turkey, in 1976, 1979, and 1985, respectively. He conducted his Ph.D. research under the supervision of Prof. E. Ozkarahan; at Arizona State University, Tempe, AZ, USA, and Intel, Chandler, AZ, USA; as a part of the RAP Database Machine Project. He is currently a Faculty Member at Bilkent University, Ankara. Before joining Bilkent, he was a tenured Full Professor at Miami University, Oxford, OH, USA. He co-edited ACM SIGIR Forum from 1995 to 2002 and is a Co-Founder of the Bilkent Information Retrieval Group, Bilkent University. His interest in dynamic information processing dates back to his 1993 incremental clustering paper in ACM Transactions on Information Systems and some other earlier work with Prof. E. Ozkarahan on dynamic cluster maintenance. His current research interests include information retrieval and data mining. His personal Web page is available at http://www.cs.bilkent.edu.tr/~canf.

Presentation Files