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        <title>Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets</title>
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        <namePart type="given">Vatsal</namePart>
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        <namePart type="given">Aakash</namePart>
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    <name type="personal">
        <namePart type="given">Muskaan</namePart>
        <namePart type="family">Singh</namePart>
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    <abstract>This paper presents our submission for the Shared Task-2 of classification of stance and premise in tweets about health mandates related to COVID-19 at the Social Media Mining for Health 2022. There have been a plethora of tweets about people expressing their opinions on the COVID-19 epidemic since it first emerged. The shared task emphasizes finding the level of cooperation within the mandates for their stance towards the health orders of the pandemic. Overall the shared subjects the participants to propose system’s that can efficiently perform 1) Stance Detection, which focuses on determining the author’s point of view in the text. 2) Premise Classification, which indicates whether or not the text has arguments. Through this paper we propose an orchestration of multiple transformer based encoders to derive the output for stance and premise classification. Our best model achieves a F1 score of 0.771 for Premise Classification and an aggregate macro-F1 score of 0.661 for Stance Detection. We have made our code public here</abstract>
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%0 Conference Proceedings
%T Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets
%A Savaliya, Vatsal
%A Bhatnagar, Aakash
%A Bhavsar, Nidhir
%A Singh, Muskaan
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F savaliya-etal-2022-innovators
%X This paper presents our submission for the Shared Task-2 of classification of stance and premise in tweets about health mandates related to COVID-19 at the Social Media Mining for Health 2022. There have been a plethora of tweets about people expressing their opinions on the COVID-19 epidemic since it first emerged. The shared task emphasizes finding the level of cooperation within the mandates for their stance towards the health orders of the pandemic. Overall the shared subjects the participants to propose system’s that can efficiently perform 1) Stance Detection, which focuses on determining the author’s point of view in the text. 2) Premise Classification, which indicates whether or not the text has arguments. Through this paper we propose an orchestration of multiple transformer based encoders to derive the output for stance and premise classification. Our best model achieves a F1 score of 0.771 for Premise Classification and an aggregate macro-F1 score of 0.661 for Stance Detection. We have made our code public here
%U https://aclanthology.org/2022.smm4h-1.35
%P 126-129

Markdown (Informal)

[Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets](https://aclanthology.org/2022.smm4h-1.35) (Savaliya et al., SMM4H 2022)

• Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets (Savaliya et al., SMM4H 2022)

ACL

• Vatsal Savaliya, Aakash Bhatnagar, Nidhir Bhavsar, and Muskaan Singh. 2022. Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 126–129, Gyeongju, Republic of Korea. Association for Computational Linguistics.