Data and models for stance and premise detection in COVID-19 tweets:
  insights from the Social Media Mining for Health (SMM4H) 2022 shared task
Vera Davydova Huabin Yang Elena Tutubalina
The COVID-19 pandemic has sparked numerous discussions on social media
platforms, with users sharing their views on topics such as mask-wearing and
vaccination. To facilitate the evaluation of neural models for stance detection
and premise classification, we organized the Social Media Mining for Health
(SMM4H) 2022 Shared Task 2. This competition utilized manually annotated posts
on three COVID-19-related topics: school closures, stay-at-home orders, and
wearing masks. In this paper, we extend the previous work and present newly
collected data on vaccination from Twitter to assess the performance of models
on a different topic. To enhance the accuracy and effectiveness of our
evaluation, we employed various strategies to aggregate tweet texts with
claims, including models with feature-level (early) fusion and dual-view
architectures from SMM4H 2022 leaderboard. Our primary objective was to create
a valuable dataset and perform an extensive experimental evaluation to support
future research in argument mining in the health domain.