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        <title>Innovators @ SMM4H‘22: An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets</title>
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        <namePart type="given">Mohammad</namePart>
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    <name type="personal">
        <namePart type="given">Nidhir</namePart>
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    <name type="personal">
        <namePart type="given">Muskaan</namePart>
        <namePart type="family">Singh</namePart>
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            <title>Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop &amp; Shared Task</title>
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    <abstract>With the Surge in COVID-19, the number of social media postings related to the vaccine has grown, specifically tracing the confirmed reports by the users regarding the COVID-19 vaccine dose termed “Vaccine Surveillance.” To mitigate this research problem, we present our novel ensembled approach for self-reporting COVID-19 vaccination status tweets into two labels, namely “Vaccine Chatter” and “Self Report.” We utilize state-of-the-art models, namely BERT, RoBERTa, and XLNet. Our model provides promising results with 0.77, 0.93, and 0.66 as precision, recall, and F1-score (respectively), comparable to the corresponding median scores of 0.77, 0.9, and 0.68 (respec- tively). The model gave an overall accuracy of 93.43. We also present an empirical analysis of the results to present how well the tweet was able to classify and report. We release our code base here https://github.com/Zohair0209/SMM4H-2022-Task6.git</abstract>
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%0 Conference Proceedings
%T Innovators @ SMM4H‘22: An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets
%A Zohair, Mohammad
%A Bhavsar, Nidhir
%A Bhatnagar, Aakash
%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 zohair-etal-2022-innovators
%X With the Surge in COVID-19, the number of social media postings related to the vaccine has grown, specifically tracing the confirmed reports by the users regarding the COVID-19 vaccine dose termed “Vaccine Surveillance.” To mitigate this research problem, we present our novel ensembled approach for self-reporting COVID-19 vaccination status tweets into two labels, namely “Vaccine Chatter” and “Self Report.” We utilize state-of-the-art models, namely BERT, RoBERTa, and XLNet. Our model provides promising results with 0.77, 0.93, and 0.66 as precision, recall, and F1-score (respectively), comparable to the corresponding median scores of 0.77, 0.9, and 0.68 (respec- tively). The model gave an overall accuracy of 93.43. We also present an empirical analysis of the results to present how well the tweet was able to classify and report. We release our code base here https://github.com/Zohair0209/SMM4H-2022-Task6.git
%U https://aclanthology.org/2022.smm4h-1.34/
%P 123-125

Markdown (Informal)

[Innovators @ SMM4H’22: An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets](https://aclanthology.org/2022.smm4h-1.34/) (Zohair et al., SMM4H 2022)

• Innovators @ SMM4H’22: An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets (Zohair et al., SMM4H 2022)

ACL

• Mohammad Zohair, Nidhir Bhavsar, Aakash Bhatnagar, and Muskaan Singh. 2022. Innovators @ SMM4H’22: An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 123–125, Gyeongju, Republic of Korea. Association for Computational Linguistics.