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The COVID-19 pandemic has sparked numerous discussions on social mediaplatforms, with users sharing their views on topics such as mask-wearing andvaccination. To facilitate the evaluation of neural models for stance detectionand premise classification, we organized the Social Media Mining for Health(SMM4H) 2022 Shared Task 2. This competition utilized manually annotated postson three COVID-19-related topics: school closures, stay-at-home orders, andwearing masks. In this paper, we extend the previous work and present newlycollected data on vaccination from Twitter to assess the performance of modelson a different topic. To enhance the accuracy and effectiveness of ourevaluation, we employed various strategies to aggregate tweet texts withclaims, including models with feature-level (early) fusion and dual-viewarchitectures from SMM4H 2022 leaderboard. Our primary objective was to createa valuable dataset and perform an extensive experimental evaluation to supportfuture research in argument mining in the health domain.

Data and models for stance and premise detection in COVID-19 tweets: insights from the Social Media Mining for Health (SMM4H) 2022 shared task