yiriyou@ SMM 4 H ’22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks (2022)

Abstract: The paper introduces the methodology proposed for the shared Task 2 of the Social Media Mining for Health Application (SMM4H) in 2022. Task 2 consists of two subtasks: Stance Detection and Premise Classification, named Subtask 2a and Subtask 2b, respectively. Our proposed system is based on dual-view attention neural networks and achieves an F1 score of 0.618 for Subtask 2a (0.068 more than the median) and an F1 score of 0.630 for Subtask 2b (0.017 less than the median). Further experiments show that the domain-specific pre-trained model, cross-validation, and pseudo-label techniques contribute to the improvement of system performance.

## introduction since the outbreak of the covid-19 pandemic, there has been a large amount of research on covid-19 in the natural language processing (nlp) arena. meanwhile, social media platforms such as twitter are widely used to share and spread users' views on various issues, so the social media mining for health application (smm4h) shared task in 2022 @xcite focuses on leveraging covid-related tweets for health research. in this paper, we describe our methodology for the shared task 2: classification of stance and premise in tweets about health mandates related to covid-19 (in english). we are provided with labeled training set containing texts from twitter about three health mandates (claims) related to covid-19 pandemic: school closures, stay at home orders and face masks 1 . subtask 2a aims at detecting the stance of the text's author concerning the given claim (e.g., school closures), while subtask 2b aims at identifying whether there are premises in an argument. * *corresponding author 1 see @xcite for more details 2 methodology