Interactively Uncovering Latent Arguments in Social Media Platforms: A Case Study on the Covid-19 Vaccine Debate

Maria Leonor Pacheco, Tunazzina Islam, Lyle Ungar, Ming Yin, Dan Goldwasser


Abstract
Automated methods for analyzing public opinion have grown in popularity with the proliferation of social media. While supervised methods can be very good at classifying text, the dynamic nature of social media discourse results in a moving target for supervised learning. Meanwhile, traditional unsupervised techniques for extracting themes from textual repositories, such as topic models, can result in incorrect outputs that are unusable to domain experts. For this reason, a non-trivial amount of research on social media discourse still relies on manual coding techniques. In this paper, we present an interactive, humans-in-the-loop framework that strikes a balance between unsupervised techniques and manual coding for extracting latent arguments from social media discussions. We use the COVID-19 vaccination debate as a case study, and show that our methodology can be used to obtain a more accurate, interpretable set of arguments when compared to traditional topic models. We do this at a relatively low manual cost, as 3 experts take approximately 2 hours to code close to 100k tweets.
Anthology ID:
2022.dash-1.13
Volume:
Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Eduard Dragut, Yunyao Li, Lucian Popa, Slobodan Vucetic, Shashank Srivastava
Venue:
DaSH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
94–111
Language:
URL:
https://aclanthology.org/2022.dash-1.13
DOI:
Bibkey:
Cite (ACL):
Maria Leonor Pacheco, Tunazzina Islam, Lyle Ungar, Ming Yin, and Dan Goldwasser. 2022. Interactively Uncovering Latent Arguments in Social Media Platforms: A Case Study on the Covid-19 Vaccine Debate. In Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances), pages 94–111, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Interactively Uncovering Latent Arguments in Social Media Platforms: A Case Study on the Covid-19 Vaccine Debate (Pacheco et al., DaSH 2022)
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PDF:
https://aclanthology.org/2022.dash-1.13.pdf
Video:
 https://aclanthology.org/2022.dash-1.13.mp4