Detection and Characterization of Stance on Social Media
Stance detection involves the identification of the positions of a piece of text or a user towards a target such as a topic, entity, or claim. A growing body of research in the ICWSM and Social Computing community on performing and using stance detection shows its importance for a variety of applications including properly analyzing the attitudes of online users.
This tutorial aims to teach participants how to perform and use stance detection. Specifically, we provide a general introduction to the concept of stance and how it differs from sentiment analysis; present recent methodologies for stance detection on social media including supervised, semi-supervised, and unsupervised methods; and introduce various applications of stance detection on social media including how it can be used to support analytical studies. The tutorial concludes with an exploration of open challenges and future directions for stance detection on social media.
Abeer AlDayel PhD student at the school of Informatics, the University of Edinburgh. Her work is on stanc detection on social media. Website: https://abeeraldayel.github.io/
Kareem Darwish Principle scientist at Qatar Computing Research Institute, HBKU university. Website: http://kareemdarwish.com/
Walid Magdy Associate professor at the School of Informatics, the University of Edinburgh, and faculty fellow at the Alan Turing Institute. Website: http://homepages.inf.ed.ac.uk/wmagdy/
- part1:What is Stance Detection?
- part2_phase1:Stance Detection Modeling
- part2_phase2: Stance Detection Methods
- part3: Stance detection applications